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2020 – today
- 2024
[j192]Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto:
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review. J. Medical Syst. 48(1): 84 (2024)- [j191]Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen
, Ning Xie, Gang Niu, Masashi Sugiyama:
Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks 180: 106741 (2024) - [j190]Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 2569-2583 (2024) - [j189]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4398-4409 (2024) - [j188]Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama:
On the Effectiveness of Adversarial Training Against Backdoor Attacks. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14878-14888 (2024) - [c296]Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama:
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models. AAAI 2024: 13383-13390 - [c295]Shintaro Nakamura, Masashi Sugiyama:
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. AAAI 2024: 14414-14421 - [c294]Guillaume Braun, Masashi Sugiyama:
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. AISTATS 2024: 532-540 - [c293]Shintaro Nakamura, Masashi Sugiyama:
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. AISTATS 2024: 1225-1233 - [c292]Masashi Sugiyama:
Overcoming Continuous Distribution Shifts: Challenges in Online Machine Learning. BCI 2024: 1-3 - [c291]Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama:
Direct Distillation Between Different Domains. ECCV (80) 2024: 154-172 - [c290]Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama:
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. EMNLP 2024: 14394-14410 - [c289]Shuo Chen, Gang Niu, Chen Gong, Okan Koc, Jian Yang, Masashi Sugiyama:
Robust Similarity Learning with Difference Alignment Regularization. ICLR 2024 - [c288]Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. ICLR 2024 - [c287]Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang, Kunda Yan, Bo Han, Gang Niu, Lei Fang, Changshui Zhang, Masashi Sugiyama:
Accurate Forgetting for Heterogeneous Federated Continual Learning. ICLR 2024 - [c286]Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
A General Framework for Learning from Weak Supervision. ICML 2024 - [c285]Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. ICML 2024 - [c284]Yuyang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou:
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. ICML 2024 - [c283]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. ICML 2024 - [c282]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. ICML 2024 - [c281]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. ICML 2024 - [c280]Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. ICML 2024 - [c279]Qingxiuxiong Dong, Toshimitsu Kaneko, Masashi Sugiyama:
An offline learning of behavior correction policy for vision-based robotic manipulation. ICRA 2024: 5448-5454 - [c278]Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. RLC 2024: 2140-2161 - [c277]Yuki Tanaka, Shuhei M. Yoshida, Takashi Shibata, Makoto Terao, Takayuki Okatani, Masashi Sugiyama:
Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data. WACV 2024: 2768-2777 - [i236]Jialiang Tang, Shuo Chen, Gang Niu, Hongyuan Zhu, Joey Tianyi Zhou, Chen Gong, Masashi Sugiyama:
Direct Distillation between Different Domains. CoRR abs/2401.06826 (2024) - [i235]Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
A General Framework for Learning from Weak Supervision. CoRR abs/2402.01922 (2024) - [i234]Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama:
Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution. CoRR abs/2402.03771 (2024) - [i233]Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
Generating Chain-of-Thoughts with a Direct Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought. CoRR abs/2402.06918 (2024) - [i232]Guillaume Braun, Masashi Sugiyama:
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates. CoRR abs/2402.18805 (2024) - [i231]Hao Chen, Jindong Wang, Zihan Wang, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Learning with Noisy Foundation Models. CoRR abs/2403.06869 (2024) - [i230]Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric:
Reinforcement Learning with Options and State Representation. CoRR abs/2403.10855 (2024) - [i229]Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training. CoRR abs/2404.06287 (2024) - [i228]Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama:
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. CoRR abs/2404.07465 (2024) - [i227]Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang:
Balancing Similarity and Complementarity for Federated Learning. CoRR abs/2405.09892 (2024) - [i226]Johannes Ackermann, Takayuki Osa, Masashi Sugiyama:
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity. CoRR abs/2405.14114 (2024) - [i225]Or Raveh, Junya Honda, Masashi Sugiyama:
Multi-Player Approaches for Dueling Bandits. CoRR abs/2405.16168 (2024) - [i224]Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang:
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. CoRR abs/2405.18890 (2024) - [i223]Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj:
Slight Corruption in Pre-training Data Makes Better Diffusion Models. CoRR abs/2405.20494 (2024) - [i222]Jianing Zhu, Bo Han, Jiangchao Yao, Jianliang Xu, Gang Niu, Masashi Sugiyama:
Decoupling the Class Label and the Target Concept in Machine Unlearning. CoRR abs/2406.08288 (2024) - [i221]Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama:
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR abs/2406.09179 (2024) - [i220]Jiahao Xiao, Ming-Kun Xie, Heng-Bo Fan, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning. CoRR abs/2407.18624 (2024) - [i219]Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama:
Adaptive complexity of log-concave sampling. CoRR abs/2408.13045 (2024) - [i218]Ming Li, Jike Zhong, Chenxin Li, Liuzhuozheng Li, Nie Lin, Masashi Sugiyama:
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification. CoRR abs/2409.16718 (2024) - [i217]Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao, Gang Niu, Masashi Sugiyama:
On Unsupervised Prompt Learning for Classification with Black-box Language Models. CoRR abs/2410.03124 (2024) - 2023
- [j187]Yosuke Otsubo, Naoya Otani, Megumi Chikasue, Mineyuki Nishino, Masashi Sugiyama:
Root cause estimation of faults in production processes: a novel approach inspired by approximate Bayesian computation. Int. J. Prod. Res. 61(5): 1556-1574 (2023) - [j186]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal Approximation Property of Invertible Neural Networks. J. Mach. Learn. Res. 24: 287:1-287:68 (2023) - [j185]Shota Nakajima, Masashi Sugiyama:
Positive-unlabeled classification under class-prior shift: a prior-invariant approach based on density ratio estimation. Mach. Learn. 112(3): 889-919 (2023) - [j184]Shuo Chen, Chen Gong, Xiang Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Boundary-restricted metric learning. Mach. Learn. 112(12): 4723-4762 (2023) - [j183]Zhenguo Wu, Jiaqi Lv, Masashi Sugiyama:
Learning With Proper Partial Labels. Neural Comput. 35(1): 58-81 (2023) - [j182]Tingting Zhao, S. Wu, G. Li, Y. Chen, Gang Niu, Masashi Sugiyama:
Learning Intention-Aware Policies in Deep Reinforcement Learning. Neural Comput. 35(10): 1657-1677 (2023) - [j181]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation learning for continuous action spaces is beneficial for efficient policy learning. Neural Networks 159: 137-152 (2023) - [j180]Chen Gong, Yongliang Ding, Bo Han, Gang Niu, Jian Yang, Jane You, Dacheng Tao, Masashi Sugiyama:
Class-Wise Denoising for Robust Learning Under Label Noise. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2835-2848 (2023) - [c276]Jongyeong Lee, Junya Honda, Masashi Sugiyama:
Thompson Exploration with Best Challenger Rule in Best Arm Identification. ACML 2023: 646-661 - [c275]Nobutaka Ito, Masashi Sugiyama:
Audio Signal Enhancement with Learning from Positive and Unlabeled Data. ICASSP 2023: 1-5 - [c274]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. ICCV 2023: 17225-17234 - [c273]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. ICCV 2023: 17424-17434 - [c272]Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. ICLR 2023 - [c271]Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou:
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. ICLR 2023 - [c270]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML 2023: 8260-8275 - [c269]Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. ICML 2023: 11255-11282 - [c268]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. ICML 2023: 18810-18851 - [c267]Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. ICML 2023: 41596-41612 - [c266]Xin-Qiang Cai, Pushi Zhang, Li Zhao, Jiang Bian, Masashi Sugiyama, Ashley Llorens:
Distributional Pareto-Optimal Multi-Objective Reinforcement Learning. NeurIPS 2023 - [c265]Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang, Masashi Sugiyama:
Imitation Learning from Vague Feedback. NeurIPS 2023 - [c264]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. NeurIPS 2023 - [c263]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. NeurIPS 2023 - [c262]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning. NeurIPS 2023 - [c261]Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama:
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective. NeurIPS 2023 - [c260]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. NeurIPS 2023 - [c259]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. NeurIPS 2023 - [c258]Yu-Jie Zhang, Masashi Sugiyama:
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost. NeurIPS 2023 - [c257]Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama:
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. NeurIPS 2023 - [c256]Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS 2023 - [i216]Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama:
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. CoRR abs/2302.01544 (2023) - [i215]Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama:
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. CoRR abs/2302.02552 (2023) - [i214]Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon:
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. CoRR abs/2302.02907 (2023) - [i213]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection. CoRR abs/2302.03857 (2023) - [i212]Jongyeong Lee, Chao-Kai Chiang, Masashi Sugiyama:
Asymptotically Optimal Thompson Sampling Based Policy for the Uniform Bandits and the Gaussian Bandits. CoRR abs/2302.14407 (2023) - [i211]Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu:
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR abs/2303.12291 (2023) - [i210]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization. CoRR abs/2305.00374 (2023) - [i209]Jingfeng Zhang, Bo Song, Bo Han, Lei Liu, Gang Niu, Masashi Sugiyama:
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. CoRR abs/2305.00399 (2023) - [i208]Ming-Kun Xie, Jiahao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning. CoRR abs/2305.02795 (2023) - [i207]Yivan Zhang, Masashi Sugiyama:
A Category-theoretical Meta-analysis of Definitions of Disentanglement. CoRR abs/2305.06886 (2023) - [i206]Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation. CoRR abs/2305.08344 (2023) - [i205]Sora Satake, Yoshihiro Nagano, Masashi Sugiyama, Masahiro Fujiwara, Yasutoshi Makino, Hiroyuki Shinoda:
Analysis of Pleasantness Evoked by Various Airborne Ultrasound Tactile Stimuli Using Pairwise Comparisons and the Bradley-Terry Model. CoRR abs/2305.09412 (2023) - [i204]Yivan Zhang, Masashi Sugiyama:
Enriching Disentanglement: Definitions to Metrics. CoRR abs/2305.11512 (2023) - [i203]Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj:
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. CoRR abs/2305.12715 (2023) - [i202]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems. CoRR abs/2305.14690 (2023) - [i201]Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama:
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR abs/2305.18377 (2023) - [i200]Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, Tongliang Liu:
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision. CoRR abs/2306.07036 (2023) - [i199]Shintaro Nakamura, Masashi Sugiyama:
Combinatorial Pure Exploration of Multi-Armed Bandit with a Real Number Action Class. CoRR abs/2306.09202 (2023) - [i198]Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han:
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR abs/2307.05948 (2023) - [i197]Jialiang Tang, Shuo Chen, Gang Niu, Masashi Sugiyama, Chen Gong:
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images. CoRR abs/2307.11469 (2023) - [i196]Penghui Yang, Ming-Kun Xie, Chen-Chen Zong, Lei Feng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang:
Multi-Label Knowledge Distillation. CoRR abs/2308.06453 (2023) - [i195]Shintaro Nakamura, Masashi Sugiyama:
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR abs/2308.10238 (2023) - [i194]Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. CoRR abs/2309.08216 (2023) - [i193]Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, Bhiksha Raj:
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks. CoRR abs/2309.17002 (2023) - [i192]Jongyeong Lee, Junya Honda, Masashi Sugiyama:
Thompson Exploration with Best Challenger Rule in Best Arm Identification. CoRR abs/2310.00539 (2023) - [i191]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. CoRR abs/2310.05632 (2023) - [i190]Wentao Yu, Shuo Chen, Chen Gong, Gang Niu, Masashi Sugiyama:
Atom-Motif Contrastive Transformer for Molecular Property Prediction. CoRR abs/2310.07351 (2023) - [i189]Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han:
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. CoRR abs/2310.13923 (2023) - [i188]Shintaro Nakamura, Masashi Sugiyama:
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit. CoRR abs/2310.15681 (2023) - [i187]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR abs/2311.15502 (2023) - 2022
- [j179]Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima:
Improving imbalanced classification using near-miss instances. Expert Syst. Appl. 201: 117130 (2022) - [j178]Hiroki Ishiguro, Takashi Ishida, Masashi Sugiyama:
Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Trans. Inf. Syst. 105-D(2): 364-376 (2022) - [j177]Yuangang Pan, Ivor W. Tsang, Weijie Chen, Gang Niu, Masashi Sugiyama:
Fast and Robust Rank Aggregation against Model Misspecification. J. Mach. Learn. Res. 23: 23:1-23:35 (2022) - [j176]Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama:
Learning from Noisy Pairwise Similarity and Unlabeled Data. J. Mach. Learn. Res. 23: 307:1-307:34 (2022) - [j175]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks 152: 90-104 (2022) - [j174]Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto:
Deep learning, reinforcement learning, and world models. Neural Networks 152: 267-275 (2022) - [j173]Kenji Doya, Karl J. Friston, Masashi Sugiyama, Joshua B. Tenenbaum:
Neural Networks special issue on Artificial Intelligence and Brain Science. Neural Networks 155: 328-329 (2022) - [j172]Chen Gong, Jian Yang, Jane You, Masashi Sugiyama:
Centroid Estimation With Guaranteed Efficiency: A General Framework for Weakly Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(6): 2841-2855 (2022) - [j171]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3590-3601 (2022) - [j170]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Lizhen Cui, Gang Niu, Masashi Sugiyama:
NoiLin: Improving adversarial training and correcting stereotype of noisy labels. Trans. Mach. Learn. Res. 2022 (2022) - [c255]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. ACML 2022: 770-785 - [c254]Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama:
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization. ACML 2022: 990-1005 - [c253]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Pairwise Supervision Can Provably Elicit a Decision Boundary. AISTATS 2022: 2618-2640 - [c252]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Predictive variational Bayesian inference as risk-seeking optimization. AISTATS 2022: 5051-5083 - [c251]Masashi Sugiyama, Tongliang Liu, Bo Han, Yang Liu, Gang Niu:
Learning and Mining with Noisy Labels. CIKM 2022: 5152-5155 - [c250]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CVPR 2022: 16609-16618 - [c249]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data. ICLR 2022 - [c248]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients. ICLR 2022 - [c247]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. ICLR 2022 - [c246]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning. ICLR 2022 - [c245]Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama:
Exploiting Class Activation Value for Partial-Label Learning. ICLR 2022 - [c244]Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, Yang Liu:
To Smooth or Not? When Label Smoothing Meets Noisy Labels. ICML 2022: 23589-23614 - [c243]Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama:
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum. ICML 2022: 24430-24459 - [c242]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. ICML 2022: 24743-24769 - [c241]Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan:
Towards Adversarially Robust Deep Image Denoising. IJCAI 2022: 1516-1522 - [c240]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. NeurIPS 2022 - [c239]Shuo Chen, Chen Gong, Jun Li, Jian Yang, Gang Niu, Masashi Sugiyama:
Learning Contrastive Embedding in Low-Dimensional Space. NeurIPS 2022 - [c238]Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou:
Adapting to Online Label Shift with Provable Guarantees. NeurIPS 2022 - [c237]Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, Masashi Sugiyama:
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses. NeurIPS 2022 - [c236]Sen Cui, Jingfeng Zhang, Jian Liang, Bo Han, Masashi Sugiyama, Changshui Zhang:
Synergy-of-Experts: Collaborate to Improve Adversarial Robustness. NeurIPS 2022 - [i186]Hanshu Yan, Jingfeng Zhang, Jiashi Feng, Masashi Sugiyama, Vincent Y. F. Tan:
Towards Adversarially Robust Deep Image Denoising. CoRR abs/2201.04397 (2022) - [i185]Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama:
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR abs/2202.00395 (2022) - [i184]Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan S. Kankanhalli:
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests. CoRR abs/2202.03077 (2022) - [i183]Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama:
On the Effectiveness of Adversarial Training against Backdoor Attacks. CoRR abs/2202.10627 (2022) - [i182]Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama:
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients. CoRR abs/2204.03304 (2022) - [i181]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal approximation property of invertible neural networks. CoRR abs/2204.07415 (2022) - [i180]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Excess risk analysis for epistemic uncertainty with application to variational inference. CoRR abs/2206.01606 (2022) - [i179]De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama:
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation. CoRR abs/2206.02791 (2022) - [i178]Charles Riou, Junya Honda, Masashi Sugiyama:
The Survival Bandit Problem. CoRR abs/2206.03019 (2022) - [i177]Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama:
Learning from Multiple Unlabeled Datasets with Partial Risk Regularization. CoRR abs/2207.01555 (2022) - [i176]Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou:
Adapting to Online Label Shift with Provable Guarantees. CoRR abs/2207.02121 (2022) - [i175]Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama:
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. CoRR abs/2208.02011 (2022) - [i174]Nobutaka Ito, Masashi Sugiyama:
Audio Signal Enhancement with Learning from Positive and Unlabelled Data. CoRR abs/2210.15143 (2022) - [i173]Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama:
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. CoRR abs/2211.00269 (2022) - [i172]Tingting Zhao, Ying Wang, Wei Sun, Yarui Chen, Gang Niu, Masashi Sugiyama:
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning. CoRR abs/2211.13257 (2022) - [i171]Shintaro Nakamura, Han Bao, Masashi Sugiyama:
Robust computation of optimal transport by β-potential regularization. CoRR abs/2212.13251 (2022) - 2021
- [j169]Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt:
Constraint learning for control tasks with limited duration barrier functions. Autom. 127: 109504 (2021) - [j168]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33(1): 244-268 (2021) - [j167]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification From Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. Neural Comput. 33(5): 1234-1268 (2021) - [j166]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. 33(8): 2128-2162 (2021) - [j165]Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama:
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. Neural Comput. 33(8): 2163-2192 (2021) - [j164]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33(12): 3361-3412 (2021) - [j163]Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama:
A One-Step Approach to Covariate Shift Adaptation. SN Comput. Sci. 2(4): 319 (2021) - [c235]Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. AISTATS 2021: 298-306 - [c234]Han Bao, Masashi Sugiyama:
Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. AISTATS 2021: 1648-1656 - [c233]Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama:
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS 2021: 1783-1791 - [c232]Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients. AISTATS 2021: 4078-4086 - [c231]Masashi Sugiyama:
Mixture Proportion Estimation in Weakly Supervised Learning. CIKM Workshops 2021 - [c230]Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama:
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. CVPR 2021: 5202-5211 - [c229]Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama:
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. EACL 2021: 581-592 - [c228]Zeke Xie, Issei Sato, Masashi Sugiyama:
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. ICLR 2021 - [c227]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. ICLR 2021 - [c226]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. ICML 2021: 825-836 - [c225]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. ICML 2021: 1272-1282 - [c224]Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama:
Classification with Rejection Based on Cost-sensitive Classification. ICML 2021: 1507-1517 - [c223]Shuo Chen, Gang Niu, Chen Gong, Jun Li, Jian Yang, Masashi Sugiyama:
Large-Margin Contrastive Learning with Distance Polarization Regularizer. ICML 2021: 1673-1683 - [c222]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. ICML 2021: 2880-2891 - [c221]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. ICML 2021: 3252-3262 - [c220]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks. ICML 2021: 3564-3575 - [c219]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-noise Learning without Anchor Points. ICML 2021: 6403-6413 - [c218]Nan Lu, Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. ICML 2021: 7134-7144 - [c217]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. ICML 2021: 11448-11458 - [c216]Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama:
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. ICML 2021: 11637-11647 - [c215]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. ICML 2021: 11693-11703 - [c214]Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama:
Lower-Bounded Proper Losses for Weakly Supervised Classification. ICML 2021: 12110-12120 - [c213]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. ICML 2021: 12501-12512 - [c212]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. NeurIPS 2021: 6803-6815 - [c211]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. NeurIPS 2021: 23258-23269 - [c210]Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. ECML/PKDD (2) 2021: 233-249 - [c209]Takeshi Teshima, Masashi Sugiyama:
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. UAI 2021: 86-96 - [i170]Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
A Symmetric Loss Perspective of Reliable Machine Learning. CoRR abs/2101.01366 (2021) - [i169]Masato Ishii, Masashi Sugiyama:
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics. CoRR abs/2101.10842 (2021) - [i168]Shida Lei, Nan Lu, Gang Niu, Issei Sato, Masashi Sugiyama:
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification. CoRR abs/2102.00678 (2021) - [i167]Xuefeng Du, Jingfeng Zhang, Bo Han, Tongliang Liu, Yu Rong, Gang Niu, Junzhou Huang, Masashi Sugiyama:
Learning Diverse-Structured Networks for Adversarial Robustness. CoRR abs/2102.01886 (2021) - [i166]Xuefeng Li, Tongliang Liu, Bo Han, Gang Niu, Masashi Sugiyama:
Provably End-to-end Label-Noise Learning without Anchor Points. CoRR abs/2102.02400 (2021) - [i165]Yivan Zhang, Gang Niu, Masashi Sugiyama:
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. CoRR abs/2102.02414 (2021) - [i164]Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan S. Kankanhalli, Masashi Sugiyama:
Understanding the Interaction of Adversarial Training with Noisy Labels. CoRR abs/2102.03482 (2021) - [i163]Hanshu Yan, Jingfeng Zhang, Gang Niu, Jiashi Feng, Vincent Y. F. Tan, Masashi Sugiyama:
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection. CoRR abs/2102.05311 (2021) - [i162]Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Learning from Similarity-Confidence Data. CoRR abs/2102.06879 (2021) - [i161]Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama:
Guided Interpolation for Adversarial Training. CoRR abs/2102.07327 (2021) - [i160]Takeshi Teshima, Masashi Sugiyama:
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. CoRR abs/2103.00136 (2021) - [i159]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. CoRR abs/2103.00719 (2021) - [i158]Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama:
Lower-bounded proper losses for weakly supervised classification. CoRR abs/2103.02893 (2021) - [i157]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Discovering Diverse Solutions in Deep Reinforcement Learning. CoRR abs/2103.07084 (2021) - [i156]Yivan Zhang, Masashi Sugiyama:
Approximating Instance-Dependent Noise via Instance-Confidence Embedding. CoRR abs/2103.13569 (2021) - [i155]Zeke Xie, Li Yuan, Zhanxing Zhu, Masashi Sugiyama:
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization. CoRR abs/2103.17182 (2021) - [i154]Jingfeng Zhang, Xilie Xu, Bo Han, Tongliang Liu, Gang Niu, Lizhen Cui, Masashi Sugiyama:
NoiLIn: Do Noisy Labels Always Hurt Adversarial Training? CoRR abs/2105.14676 (2021) - [i153]Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients. CoRR abs/2105.14900 (2021) - [i152]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels. CoRR abs/2106.00445 (2021) - [i151]Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama:
Instance Correction for Learning with Open-set Noisy Labels. CoRR abs/2106.00455 (2021) - [i150]Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama:
Loss function based second-order Jensen inequality and its application to particle variational inference. CoRR abs/2106.05010 (2021) - [i149]Jiaqi Lv, Lei Feng, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama:
On the Robustness of Average Losses for Partial-Label Learning. CoRR abs/2106.06152 (2021) - [i148]Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama:
Probabilistic Margins for Instance Reweighting in Adversarial Training. CoRR abs/2106.07904 (2021) - [i147]Yuzhou Cao, Lei Feng, Senlin Shu, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama:
Multi-Class Classification from Single-Class Data with Confidences. CoRR abs/2106.08864 (2021) - [i146]Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou:
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations. CoRR abs/2106.09256 (2021) - [i145]Shota Nakajima, Masashi Sugiyama:
Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation. CoRR abs/2107.05045 (2021) - [i144]Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama:
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. CoRR abs/2107.08135 (2021) - [i143]Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR abs/2109.14676 (2021) - [i142]Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama:
Rethinking Importance Weighting for Transfer Learning. CoRR abs/2112.10157 (2021) - [i141]Zhenguo Wu, Masashi Sugiyama:
Learning with Proper Partial Labels. CoRR abs/2112.12303 (2021) - 2020
- [j162]Janya Sainui, Masashi Sugiyama:
Unsupervised key frame selection using information theory and colour histogram difference. Int. J. Bus. Intell. Data Min. 16(3): 324-344 (2020) - [j161]Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik:
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. Mach. Learn. 109(3): 513-532 (2020) - [j160]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active deep Q-learning with demonstration. Mach. Learn. 109(9-10): 1699-1725 (2020) - [j159]Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama:
Binary classification with ambiguous training data. Mach. Learn. 109(12): 2369-2388 (2020) - [j158]Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Classification from Triplet Comparison Data. Neural Comput. 32(3): 659-681 (2020) - [j157]Yuangang Pan, Ivor W. Tsang, Avinash Kumar Singh, Chin-Teng Lin, Masashi Sugiyama:
Stochastic Multichannel Ranking with Brain Dynamics Preferences. Neural Comput. 32(8): 1499-1530 (2020) - [j156]Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback. Neural Comput. 32(9): 1733-1773 (2020) - [c208]Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama:
A One-step Approach to Covariate Shift Adaptation. ACML 2020: 65-80 - [c207]Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. AISTATS 2020: 1115-1125 - [c206]Han Bao, Masashi Sugiyama:
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. AISTATS 2020: 2337-2347 - [c205]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. COLT 2020: 408-451 - [c204]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. ICML 2020: 1929-1938 - [c203]Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama:
Learning with Multiple Complementary Labels. ICML 2020: 3072-3081 - [c202]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics. ICML 2020: 3337-3347 - [c201]Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. ICML 2020: 4006-4016 - [c200]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020: 4604-4614 - [c199]Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
Online Dense Subgraph Discovery via Blurred-Graph Feedback. ICML 2020: 5522-5532 - [c198]Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. ICML 2020: 6500-6510 - [c197]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
Variational Imitation Learning with Diverse-quality Demonstrations. ICML 2020: 9407-9417 - [c196]Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. ICML 2020: 9458-9469 - [c195]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis. ICML 2020: 9636-9647 - [c194]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. ICML 2020: 11278-11287 - [c193]Kazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama:
Binary Classification from Positive Data with Skewed Confidence. IJCAI 2020: 3328-3334 - [c192]Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama:
Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. IROS 2020: 9705-9711 - [c191]Jie Luo, Sarah F. Frisken, Duo Wang, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III:
Are Registration Uncertainty and Error Monotonically Associated? MICCAI (3) 2020: 264-274 - [c190]Marcus Nordström, Han Bao, Fredrik Löfman, Henrik Hult, Atsuto Maki, Masashi Sugiyama:
Calibrated Surrogate Maximization of Dice. MICCAI (4) 2020: 269-278 - [c189]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Rethinking Importance Weighting for Deep Learning under Distribution Shift. NeurIPS 2020 - [c188]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. NeurIPS 2020 - [c187]Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. NeurIPS 2020 - [c186]Taira Tsuchiya, Junya Honda, Masashi Sugiyama:
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. NeurIPS 2020 - [c185]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Part-dependent Label Noise: Towards Instance-dependent Label Noise. NeurIPS 2020 - [c184]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. NeurIPS 2020 - [c183]Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama:
Learning from Aggregate Observations. NeurIPS 2020 - [c182]Masato Ishii, Takashi Takenouchi, Masashi Sugiyama:
Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes. WACV 2020: 3041-3049 - [e5]Sinno Jialin Pan, Masashi Sugiyama:
Proceedings of The 12th Asian Conference on Machine Learning, ACML 2020, 18-20 November 2020, Bangkok, Thailand. Proceedings of Machine Learning Research 129, PMLR 2020 [contents] - [i140]Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Confidence Scores Make Instance-dependent Label-noise Learning Possible. CoRR abs/2001.03772 (2020) - [i139]Kazuhiko Shinoda, Hirotaka Kaji, Masashi Sugiyama:
Binary Classification from Positive Data with Skewed Confidence. CoRR abs/2001.10642 (2020) - [i138]Soham Dan, Han Bao, Masashi Sugiyama:
Learning from Noisy Similar and Dissimilar Data. CoRR abs/2002.00995 (2020) - [i137]Zeke Xie, Issei Sato, Masashi Sugiyama:
A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast. CoRR abs/2002.03495 (2020) - [i136]Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. CoRR abs/2002.03497 (2020) - [i135]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao:
Towards Mixture Proportion Estimation without Irreducibility. CoRR abs/2002.03673 (2020) - [i134]Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama:
Progressive Identification of True Labels for Partial-Label Learning. CoRR abs/2002.08053 (2020) - [i133]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR abs/2002.08709 (2020) - [i132]Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan S. Kankanhalli:
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CoRR abs/2002.11242 (2020) - [i131]Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami, Issei Sato, Junya Honda, Masashi Sugiyama:
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time. CoRR abs/2003.04691 (2020) - [i130]Jie Luo, Guangshen Ma, Sarah F. Frisken, Parikshit Juvekar, Nazim Haouchine, Zhe Xu, Yiming Xiao, Alexandra J. Golby, Patrick J. Codd, Masashi Sugiyama, William M. Wells III:
Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation? CoRR abs/2003.09483 (2020) - [i129]Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama:
Learning from Aggregate Observations. CoRR abs/2004.06316 (2020) - [i128]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR abs/2005.13748 (2020) - [i127]Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama:
Rethinking Importance Weighting for Deep Learning under Distribution Shift. CoRR abs/2006.04662 (2020) - [i126]Han Bao, Takuya Shimada, Liyuan Xu, Issei Sato, Masashi Sugiyama:
Similarity-based Classification: Connecting Similarity Learning to Binary Classification. CoRR abs/2006.06207 (2020) - [i125]Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama:
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. CoRR abs/2006.07805 (2020) - [i124]Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama:
Parts-dependent Label Noise: Towards Instance-dependent Label Noise. CoRR abs/2006.07836 (2020) - [i123]Kei Mukaiyama, Issei Sato, Masashi Sugiyama:
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning. CoRR abs/2006.08306 (2020) - [i122]Taira Tsuchiya, Junya Honda, Masashi Sugiyama:
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. CoRR abs/2006.09668 (2020) - [i121]Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. CoRR abs/2006.11469 (2020) - [i120]Mehdi Abbana Bennani, Masashi Sugiyama:
Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent. CoRR abs/2006.11942 (2020) - [i119]Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
Online Dense Subgraph Discovery via Blurred-Graph Feedback. CoRR abs/2006.13642 (2020) - [i118]Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama:
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia. CoRR abs/2006.15815 (2020) - [i117]Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels. CoRR abs/2007.02235 (2020) - [i116]Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama:
A One-step Approach to Covariate Shift Adaptation. CoRR abs/2007.04043 (2020) - [i115]Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama:
Provably Consistent Partial-Label Learning. CoRR abs/2007.08929 (2020) - [i114]Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan S. Kankanhalli:
Geometry-aware Instance-reweighted Adversarial Training. CoRR abs/2010.01736 (2020) - [i113]Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama:
Pointwise Binary Classification with Pairwise Confidence Comparisons. CoRR abs/2010.01875 (2020) - [i112]Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. CoRR abs/2010.10181 (2020) - [i111]Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Masashi Sugiyama:
Maximum Mean Discrepancy is Aware of Adversarial Attacks. CoRR abs/2010.11415 (2020) - [i110]Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama:
Classification with Rejection Based on Cost-sensitive Classification. CoRR abs/2010.11748 (2020) - [i109]Naoya Otani, Yosuke Otsubo, Tetsuya Koike, Masashi Sugiyama:
Binary classification with ambiguous training data. CoRR abs/2011.02598 (2020) - [i108]Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama:
A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR abs/2011.04406 (2020) - [i107]Zeke Xie, Fengxiang He, Shaopeng Fu, Issei Sato, Dacheng Tao, Masashi Sugiyama:
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting. CoRR abs/2011.06220 (2020) - [i106]Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama:
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective. CoRR abs/2011.09172 (2020) - [i105]Zeke Xie, Issei Sato, Masashi Sugiyama:
Stable Weight Decay Regularization. CoRR abs/2011.11152 (2020) - [i104]Yuko Kuroki, Junya Honda, Masashi Sugiyama:
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation. CoRR abs/2012.15584 (2020)
2010 – 2019
- 2019
- [j155]Masashi Sugiyama, Yung-Kyun Noh:
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). Mach. Learn. 108(5): 717-719 (2019) - [j154]Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, Masashi Sugiyama:
Good arm identification via bandit feedback. Mach. Learn. 108(5): 721-745 (2019) - [j153]Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama:
Millionaire: a hint-guided approach for crowdsourcing. Mach. Learn. 108(5): 831-858 (2019) - [j152]Hirotaka Kaji, Hisashi Iizuka, Masashi Sugiyama:
ECG-Based Concentration Recognition With Multi-Task Regression. IEEE Trans. Biomed. Eng. 66(1): 101-110 (2019) - [c181]Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama:
Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization. AAAI 2019: 2304-2313 - [c180]Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Bayesian Posterior Approximation via Greedy Particle Optimization. AAAI 2019: 3606-3613 - [c179]Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama:
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy. AAAI 2019: 4122-4129 - [c178]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: A Remedy for Ceiling Effects. AAAI 2019: 5151-5158 - [c177]Liyuan Xu, Junya Honda, Masashi Sugiyama:
Dueling Bandits with Qualitative Feedback. AAAI 2019: 5549-5556 - [c176]Masato Ishii, Takashi Takenouchi, Masashi Sugiyama:
Zero-shot Domain Adaptation Based on Attribute Information. ACML 2019: 473-488 - [c175]Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama:
Learning Only from Relevant Keywords and Unlabeled Documents. EMNLP/IJCNLP (1) 2019: 3991-4000 - [c174]Hirotaka Kaji, Masashi Sugiyama:
Binary Classification Only from Unlabeled Data by Iterative Unlabeled-unlabeled Classification. ICASSP 2019: 3527-3531 - [c173]Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki:
Learning Efficient Tensor Representations with Ring-structured Networks. ICASSP 2019: 8608-8612 - [c172]Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. ICLR (Poster) 2019 - [c171]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. ICLR (Poster) 2019 - [c170]Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
On Symmetric Losses for Learning from Corrupted Labels. ICML 2019: 961-970 - [c169]Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama:
Classification from Positive, Unlabeled and Biased Negative Data. ICML 2019: 2820-2829 - [c168]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. ICML 2019: 2971-2980 - [c167]Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. ICML 2019: 6818-6827 - [c166]Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? ICML 2019: 7164-7173 - [c165]Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III, Sarah F. Frisken:
On the Applicability of Registration Uncertainty. MICCAI (2) 2019: 410-419 - [c164]Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama:
On the Calibration of Multiclass Classification with Rejection. NeurIPS 2019: 2582-2592 - [c163]Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama:
Uncoupled Regression from Pairwise Comparison Data. NeurIPS 2019: 3994-4004 - [c162]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? NeurIPS 2019: 6835-6846 - [c161]Nontawat Charoenphakdee, Masashi Sugiyama:
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. SDM 2019: 271-279 - [p2]Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Unsupervised Discrete Representation Learning. Explainable AI 2019: 97-119 - [e4]Kamalika Chaudhuri, Masashi Sugiyama:
The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan. Proceedings of Machine Learning Research 89, PMLR 2019 [contents] - [i103]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. CoRR abs/1901.01365 (2019) - [i102]Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? CoRR abs/1901.04215 (2019) - [i101]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks using PAC-Bayesian Analysis. CoRR abs/1901.04653 (2019) - [i100]Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
On Symmetric Losses for Learning from Corrupted Labels. CoRR abs/1901.09314 (2019) - [i99]Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. CoRR abs/1901.09387 (2019) - [i98]Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik:
An analytic formulation for positive-unlabeled learning via weighted integral probability metric. CoRR abs/1901.09503 (2019) - [i97]Miao Xu, Bingcong Li, Gang Niu, Bo Han, Masashi Sugiyama:
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative. CoRR abs/1901.10155 (2019) - [i96]Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki, Masashi Sugiyama:
Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation. CoRR abs/1901.10654 (2019) - [i95]Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama:
On Possibility and Impossibility of Multiclass Classification with Rejection. CoRR abs/1901.10655 (2019) - [i94]Christian J. Walder, Richard Nock, Cheng Soon Ong, Masashi Sugiyama:
New Tricks for Estimating Gradients of Expectations. CoRR abs/1901.11311 (2019) - [i93]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR abs/1901.11351 (2019) - [i92]Takuo Kaneko, Issei Sato, Masashi Sugiyama:
Online Multiclass Classification Based on Prediction Margin for Partial Feedback. CoRR abs/1902.01056 (2019) - [i91]Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama:
Polynomial-time Algorithms for Combinatorial Pure Exploration with Full-bandit Feedback. CoRR abs/1902.10582 (2019) - [i90]Masato Ishii, Takashi Takenouchi, Masashi Sugiyama:
Zero-shot Domain Adaptation Based on Attribute Information. CoRR abs/1903.05312 (2019) - [i89]Takuya Shimada, Han Bao, Issei Sato, Masashi Sugiyama:
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization. CoRR abs/1904.11717 (2019) - [i88]Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama:
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR abs/1905.07720 (2019) - [i87]Kenshin Abe, Zijian Xu, Issei Sato, Masashi Sugiyama:
Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge. CoRR abs/1905.11623 (2019) - [i86]Yuangang Pan, Weijie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
Fast and Robust Rank Aggregation against Model Misspecification. CoRR abs/1905.12341 (2019) - [i85]Han Bao, Masashi Sugiyama:
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification. CoRR abs/1905.12511 (2019) - [i84]Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama:
Uncoupled Regression from Pairwise Comparison Data. CoRR abs/1905.13659 (2019) - [i83]Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama:
Are Anchor Points Really Indispensable in Label-Noise Learning? CoRR abs/1906.00189 (2019) - [i82]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR abs/1907.09588 (2019) - [i81]Zhenghang Cui, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Classification from Triplet Comparison Data. CoRR abs/1907.10225 (2019) - [i80]Jie Luo, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III, Sarah F. Frisken:
Pilot Study on Verifying the Monotonic Relationship between Error and Uncertainty in Deformable Registration for Neurosurgery. CoRR abs/1908.07709 (2019) - [i79]Motoya Ohnishi, Gennaro Notomista, Masashi Sugiyama, Magnus Egerstedt:
Constraint Learning for Control Tasks with Limited Duration Barrier Functions. CoRR abs/1908.09506 (2019) - [i78]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR abs/1909.06769 (2019) - [i77]Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama:
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics. CoRR abs/1910.01465 (2019) - [i76]Nontawat Charoenphakdee, Jongyeong Lee, Yiping Jin, Dittaya Wanvarie, Masashi Sugiyama:
Learning Only from Relevant Keywords and Unlabeled Documents. CoRR abs/1910.04385 (2019) - [i75]Yivan Zhang, Nontawat Charoenphakdee, Masashi Sugiyama:
Learning from Indirect Observations. CoRR abs/1910.04394 (2019) - [i74]Paavo Parmas, Masashi Sugiyama:
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme. CoRR abs/1910.06419 (2019) - [i73]Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama:
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach. CoRR abs/1910.08974 (2019) - [i72]Alon Jacovi, Gang Niu, Yoav Goldberg, Masashi Sugiyama:
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning. CoRR abs/1910.13339 (2019) - [i71]Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, Masashi Sugiyama:
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? CoRR abs/1911.08696 (2019) - 2018
- [j151]Jie Luo, Sarah F. Frisken, Inês Machado, Miaomiao Zhang, Steve Pieper, Polina Golland, Matthew Toews, Prashin Unadkat, Alireza Sedghi, Haoyin Zhou, Alireza Mehrtash, Frank Preiswerk, Cheng-Chieh Cheng, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III:
Using the variogram for vector outlier screening: application to feature-based image registration. Int. J. Comput. Assist. Radiol. Surg. 13(12): 1871-1880 (2018) - [j150]Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Stochastic Divergence Minimization for Biterm Topic Models. IEICE Trans. Inf. Syst. 101-D(3): 668-677 (2018) - [j149]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107(4): 767-794 (2018) - [j148]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107(4): 795 (2018) - [j147]Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama:
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. Neural Comput. 30(2) (2018) - [j146]Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee:
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. Neural Comput. 30(7) (2018) - [j145]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105: 132-141 (2018) - [c160]Takayuki Osa, Masashi Sugiyama:
Hierarchical Policy Search via Return-Weighted Density Estimation. AAAI 2018: 3860-3867 - [c159]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Variational Inference based on Robust Divergences. AISTATS 2018: 813-822 - [c158]Liyuan Xu, Junya Honda, Masashi Sugiyama:
A fully adaptive algorithm for pure exploration in linear bandits. AISTATS 2018: 843-851 - [c157]Hongyi Ding, Mohammad Emtiyaz Khan, Issei Sato, Masashi Sugiyama:
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling. AISTATS 2018: 1108-1116 - [c156]Hirotaka Kaji, Hayato Yamaguchi, Masashi Sugiyama:
Multi Task Learning with Positive and Unlabeled Data and its Application to Mental State Prediction. ICASSP 2018: 2301-2305 - [c155]Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama:
Guide Actor-Critic for Continuous Control. ICLR (Poster) 2018 - [c154]Qibin Zhao, Masashi Sugiyama, Longhao Yuan, Andrzej Cichocki:
Learning Efficient Tensor Representations with Ring Structure Networks. ICLR (Workshop) 2018 - [c153]Han Bao, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. ICML 2018: 461-470 - [c152]Weihua Hu, Gang Niu, Issei Sato, Masashi Sugiyama:
Does Distributionally Robust Supervised Learning Give Robust Classifiers? ICML 2018: 2034-2042 - [c151]Hideaki Imamura, Issei Sato, Masashi Sugiyama:
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. ICML 2018: 2152-2161 - [c150]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. KDD 2018: 1571-1579 - [c149]Jie Luo, Matthew Toews, Inês Machado, Sarah F. Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III:
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation. MICCAI (4) 2018: 30-38 - [c148]Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama:
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. NeurIPS 2018: 2818-2829 - [c147]Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. NeurIPS 2018: 5841-5851 - [c146]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Binary Classification from Positive-Confidence Data. NeurIPS 2018: 5921-5932 - [c145]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. NeurIPS 2018: 6542-6551 - [c144]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-teaching: Robust training of deep neural networks with extremely noisy labels. NeurIPS 2018: 8536-8546 - [c143]Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama:
Uplift Modeling from Separate Labels. NeurIPS 2018: 9949-9959 - [c142]Hongyi Ding, Young Lee, Issei Sato, Masashi Sugiyama:
Variational Inference for Gaussian Processes with Panel Count Data. UAI 2018: 290-299 - [i70]Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama:
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. CoRR abs/1802.04034 (2018) - [i69]Han Bao, Gang Niu, Masashi Sugiyama:
Classification from Pairwise Similarity and Unlabeled Data. CoRR abs/1802.04381 (2018) - [i68]Hideaki Imamura, Issei Sato, Masashi Sugiyama:
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. CoRR abs/1802.04551 (2018) - [i67]Sheng-Jun Huang, Miao Xu, Ming-Kun Xie, Masashi Sugiyama, Gang Niu, Songcan Chen:
Active Feature Acquisition with Supervised Matrix Completion. CoRR abs/1802.05380 (2018) - [i66]Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama:
Millionaire: A Hint-guided Approach for Crowdsourcing. CoRR abs/1802.09172 (2018) - [i65]Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama:
Binary Matrix Completion Using Unobserved Entries. CoRR abs/1803.04663 (2018) - [i64]Jie Luo, Sarah F. Frisken, Karteek Popuri, Dana Cobzas, Frank Preiswerk, Matthew Toews, Miaomiao Zhang, Hongyi Ding, Polina Golland, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III:
On the Ambiguity of Registration Uncertainty. CoRR abs/1803.05266 (2018) - [i63]Jie Luo, Matthew Toews, Inês Machado, Sarah F. Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra J. Golby, Masashi Sugiyama, William M. Wells III:
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation. CoRR abs/1803.07682 (2018) - [i62]Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, Masashi Sugiyama:
Co-sampling: Training Robust Networks for Extremely Noisy Supervision. CoRR abs/1804.06872 (2018) - [i61]Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama:
Frank-Wolfe Stein Sampling. CoRR abs/1805.07912 (2018) - [i60]Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor W. Tsang, Ya Zhang, Masashi Sugiyama:
Masking: A New Perspective of Noisy Supervision. CoRR abs/1805.08193 (2018) - [i59]Miao Xu, Gang Niu, Bo Han, Ivor W. Tsang, Zhi-Hua Zhou, Masashi Sugiyama:
Matrix Co-completion for Multi-label Classification with Missing Features and Labels. CoRR abs/1805.09156 (2018) - [i58]Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama:
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces. CoRR abs/1806.02985 (2018) - [i57]Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. CoRR abs/1808.10585 (2018) - [i56]Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama:
Unsupervised Domain Adaptation Based on Source-guided Discrepancy. CoRR abs/1809.03839 (2018) - [i55]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: a Remedy for Ceiling Effects. CoRR abs/1809.04997 (2018) - [i54]Liyuan Xu, Junya Honda, Masashi Sugiyama:
Dueling Bandits with Qualitative Feedback. CoRR abs/1809.05274 (2018) - [i53]Masahiro Kato, Liyuan Xu, Gang Niu, Masashi Sugiyama:
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data. CoRR abs/1809.05710 (2018) - [i52]Nontawat Charoenphakdee, Masashi Sugiyama:
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error. CoRR abs/1809.07011 (2018) - [i51]Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama:
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. CoRR abs/1809.11008 (2018) - [i50]Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama:
Classification from Positive, Unlabeled and Biased Negative Data. CoRR abs/1810.00846 (2018) - [i49]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. CoRR abs/1810.04327 (2018) - [i48]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active Deep Q-learning with Demonstration. CoRR abs/1812.02632 (2018) - 2017
- [j144]Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Namgil Lee, Ivan V. Oseledets, Masashi Sugiyama, Danilo P. Mandic:
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. Found. Trends Mach. Learn. 9(6): 431-673 (2017) - [j143]Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama:
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. 18: 180:1-180:47 (2017) - [j142]Geoffrey Holmes, Tie-Yan Liu, Hang Li, Irwin King, Masashi Sugiyama, Zhi-Hua Zhou:
Introduction: special issue of selected papers from ACML 2015. Mach. Learn. 106(4): 459-461 (2017) - [j141]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Class-prior estimation for learning from positive and unlabeled data. Mach. Learn. 106(4): 463-492 (2017) - [j140]Inbal Horev, Florian Yger, Masashi Sugiyama:
Geometry-aware principal component analysis for symmetric positive definite matrices. Mach. Learn. 106(4): 493-522 (2017) - [j139]Robert J. Durrant, Kee-Eung Kim, Geoffrey Holmes, Stephen Marsland, Masashi Sugiyama, Zhi-Hua Zhou:
Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). Mach. Learn. 106(5): 623-625 (2017) - [j138]Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi:
Homotopy continuation approaches for robust SV classification and regression. Mach. Learn. 106(7): 1009-1038 (2017) - [j137]Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama:
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction. Neural Comput. 29(8): 2076-2122 (2017) - [c141]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. AAAI 2017: 2632-2638 - [c140]Hiroaki Shiino, Hiroaki Sasaki, Gang Niu, Masashi Sugiyama:
Whitening-Free Least-Squares Non-Gaussian Component Analysis. ACML 2017: 375-390 - [c139]Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama:
Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. AISTATS 2017: 204-212 - [c138]Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama:
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. AISTATS 2017: 537-546 - [c137]Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Learning Discrete Representations via Information Maximizing Self-Augmented Training. ICML 2017: 1558-1567 - [c136]Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML 2017: 2998-3006 - [c135]Ryosuke Kamesawa, Issei Sato, Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto, Naoto Hayashi, Masashi Sugiyama:
Lung lesion detection in FDG-PET/CT with Gaussian process regression. Medical Imaging: Computer-Aided Diagnosis 2017: 101340C - [c134]Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Positive-Unlabeled Learning with Non-Negative Risk Estimator. NIPS 2017: 1675-1685 - [c133]Futoshi Futami, Issei Sato, Masashi Sugiyama:
Expectation Propagation for t-Exponential Family Using q-Algebra. NIPS 2017: 2245-2254 - [c132]Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank C. Park, Daniel D. Lee:
Generative Local Metric Learning for Kernel Regression. NIPS 2017: 2452-2462 - [c131]Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama:
Learning from Complementary Labels. NIPS 2017: 5639-5649 - [i47]Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama:
Learning Discrete Representations via Information Maximizing Self Augmented Training. CoRR abs/1702.08720 (2017) - [i46]Ryuichi Kiryo, Gang Niu, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Positive-Unlabeled Learning with Non-Negative Risk Estimator. CoRR abs/1703.00593 (2017) - [i45]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR abs/1704.06767 (2017) - [i44]Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, William M. Wells III, Masashi Sugiyama:
Misdirected Registration Uncertainty. CoRR abs/1704.08121 (2017) - [i43]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning. CoRR abs/1705.01708 (2017) - [i42]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Learning from Complementary Labels. CoRR abs/1705.07541 (2017) - [i41]Qibin Zhao, Masashi Sugiyama, Andrzej Cichocki:
Learning Efficient Tensor Representations with Ring Structure Networks. CoRR abs/1705.08286 (2017) - [i40]Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Namgil Lee, Ivan V. Oseledets, Masashi Sugiyama, Danilo P. Mandic:
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives. CoRR abs/1708.09165 (2017) - [i39]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. CoRR abs/1710.05359 (2017) - [i38]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Binary Classification from Positive-Confidence Data. CoRR abs/1710.07138 (2017) - [i37]Takayuki Osa, Masashi Sugiyama:
Hierarchical Policy Search via Return-Weighted Density Estimation. CoRR abs/1711.10173 (2017) - 2016
- [j136]Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono:
Dependence maximization localization: a novel approach to 2D street-map-based robot localization. Adv. Robotics 30(22): 1431-1445 (2016) - [j135]Hideko Kawakubo, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Computationally Efficient Class-Prior Estimation under Class Balance Change Using Energy Distance. IEICE Trans. Inf. Syst. 99-D(1): 176-186 (2016) - [j134]Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama:
An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions. Neural Comput. 28(3): 563-593 (2016) - [j133]Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama:
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. Neural Comput. 28(4): 686-715 (2016) - [j132]Hiroaki Sasaki, Yung-Kyun Noh, Gang Niu, Masashi Sugiyama:
Direct Density Derivative Estimation. Neural Comput. 28(6): 1101-1140 (2016) - [j131]Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama:
Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation. Neural Comput. 28(7): 1388-1410 (2016) - [j130]Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Model-based reinforcement learning with dimension reduction. Neural Networks 84: 1-16 (2016) - [j129]Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto:
Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning. IEEE Robotics Autom. Mag. 23(1): 96-105 (2016) - [c130]Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama:
Multitask Principal Component Analysis. ACML 2016: 302-317 - [c129]Inbal Horev, Florian Yger, Masashi Sugiyama:
Geometry-aware stationary subspace analysis. ACML 2016: 430-444 - [c128]Hiroaki Sasaki, Gang Niu, Masashi Sugiyama:
Non-Gaussian Component Analysis with Log-Density Gradient Estimation. AISTATS 2016: 1177-1185 - [c127]Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono:
Target-less camera-LiDAR extrinsic calibration using a bagged dependence estimator. CASE 2016: 1340-1347 - [c126]Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu:
Structure Learning of Partitioned Markov Networks. ICML 2016: 439-448 - [c125]Hiroaki Sasaki, Yurina Ono, Masashi Sugiyama:
Modal Regression via Direct Log-Density Derivative Estimation. ICONIP (2) 2016: 108-116 - [c124]Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama:
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. NIPS 2016: 1199-1207 - [c123]Hideko Kawakubo, Masashi Sugiyama:
Semi-supervised sufficient dimension reduction under class-prior change. TAAI 2016: 146-153 - [c122]Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama:
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI 2016 - [e3]Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett:
Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. 2016 [contents] - [i36]Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama:
Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. CoRR abs/1603.03130 (2016) - [i35]Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, Masashi Sugiyama:
Reinterpreting the Transformation Posterior in Probabilistic Image Registration. CoRR abs/1604.01889 (2016) - [i34]Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. CoRR abs/1605.06955 (2016) - [i33]Inbal Horev, Florian Yger, Masashi Sugiyama:
Geometry-aware stationary subspace analysis. CoRR abs/1605.07785 (2016) - [i32]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Class-prior Estimation for Learning from Positive and Unlabeled Data. CoRR abs/1611.01586 (2016) - [i31]Weihua Hu, Issei Sato, Masashi Sugiyama:
Robust supervised learning under uncertainty in dataset shift. CoRR abs/1611.02041 (2016) - [i30]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. CoRR abs/1611.03231 (2016) - 2015
- [b3]Masashi Sugiyama:
Statistical Reinforcement Learning - Modern Machine Learning Approaches. Chapman and Hall / CRC machine learning and pattern recognition series, CRC Press 2015, ISBN 978-1-439-85689-5, pp. I-XII, 1-192 - [j128]Hyun Ha Nam, Masashi Sugiyama:
Direct Density Ratio Estimation with Convolutional Neural Networks with Application in Outlier Detection. IEICE Trans. Inf. Syst. 98-D(5): 1073-1079 (2015) - [j127]Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Condition for perfect dimensionality recovery by variational Bayesian PCA. J. Mach. Learn. Res. 16: 3757-3811 (2015) - [j126]Cheng Soon Ong, Wray L. Buntine, Tu Bao Ho, Masashi Sugiyama, Geoffrey I. Webb:
Introduction: special issue of selected papers of ACML 2013. Mach. Learn. 99(2): 165-167 (2015) - [j125]Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama:
Direct conditional probability density estimation with sparse feature selection. Mach. Learn. 100(2-3): 161-182 (2015) - [j124]Voot Tangkaratt, Ning Xie, Masashi Sugiyama:
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. Neural Comput. 27(1): 228-254 (2015) - [j123]Marthinus Christoffel du Plessis, Hiroaki Shiino, Masashi Sugiyama:
Online Direct Density-Ratio Estimation Applied to Inlier-Based Outlier Detection. Neural Comput. 27(9): 1899-1914 (2015) - [j122]Hao Zhang, Yao Ma, Masashi Sugiyama:
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing. Neural Comput. 27(11): 2447-2475 (2015) - [j121]Makoto Yamada, Leonid Sigal, Michalis Raptis, Machiko Toyoda, Yi Chang, Masashi Sugiyama:
Cross-Domain Matching with Squared-Loss Mutual Information. IEEE Trans. Pattern Anal. Mach. Intell. 37(9): 1764-1776 (2015) - [j120]Alessandro Balzi, Florian Yger, Masashi Sugiyama:
Importance-weighted covariance estimation for robust common spatial pattern. Pattern Recognit. Lett. 68: 139-145 (2015) - [c121]Song Liu, Taiji Suzuki, Masashi Sugiyama:
Support Consistency of Direct Sparse-Change Learning in Markov Networks. AAAI 2015: 2785-2791 - [c120]Inbal Horev, Florian Yger, Masashi Sugiyama:
Geometry-Aware Principal Component Analysis for Symmetric Positive Definite Matrices. ACML 2015: 1-16 - [c119]Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama:
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. ACML 2015: 33-48 - [c118]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Class-prior Estimation for Learning from Positive and Unlabeled Data. ACML 2015: 221-236 - [c117]Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Continuous Target Shift Adaptation in Supervised Learning. ACML 2015: 285-300 - [c116]Tingting Zhao, Gang Niu, Ning Xie, Jucheng Yang, Masashi Sugiyama:
Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation. ACML 2015: 333-348 - [c115]Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama:
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. AISTATS 2015 - [c114]Florian Yger, Fabien Lotte, Masashi Sugiyama:
Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study. EUSIPCO 2015: 2721-2725 - [c113]Janya Sainui, Masashi Sugiyama:
Minimum dependency key frames selection via quadratic mutual information. ICDIM 2015: 148-153 - [c112]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Convex Formulation for Learning from Positive and Unlabeled Data. ICML 2015: 1386-1394 - [c111]Ning Xie, Tingting Zhao, Feng Tian, Xiaohua Zhang, Masashi Sugiyama:
Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning. IJCAI 2015: 2531-2539 - [c110]Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomono:
A dependence maximization approach towards street map-based localization. IROS 2015: 3721-3728 - [c109]Yukino Baba, Hisashi Kashima, Yasunobu Nohara, Eiko Kai, Partha Pratim Ghosh, Rafiqul Islam Maruf, Ashir Ahmed, Masahiro Kuroda, Sozo Inoue, Tatsuo Hiramatsu, Michio Kimura, Shuji Shimizu, Kunihisa Kobayashi, Koji Tsuda, Masashi Sugiyama, Mathieu Blondel, Naonori Ueda, Masaru Kitsuregawa, Naoki Nakashima:
Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. KDD 2015: 1681-1690 - [c108]Hao Zhang, Masashi Sugiyama:
Task selection for bandit-based task assignment in heterogeneous crowdsourcing. TAAI 2015: 164-171 - [e2]Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett:
Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada. 2015 [contents] - [r1]Neil Rubens, Mehdi Elahi, Masashi Sugiyama, Dain Kaplan:
Active Learning in Recommender Systems. Recommender Systems Handbook 2015: 809-846 - [i29]Florian Yger, Masashi Sugiyama:
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices. CoRR abs/1502.03505 (2015) - [i28]Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi:
Homotopy Continuation Approaches for Robust SV Classification and Regression. CoRR abs/1507.03229 (2015) - [i27]Hao Zhang, Yao Ma, Masashi Sugiyama:
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing. CoRR abs/1507.05800 (2015) - [i26]Hao Zhang, Masashi Sugiyama:
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing. CoRR abs/1507.07199 (2015) - [i25]Kishan Wimalawarne, Ryota Tomioka, Masashi Sugiyama:
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification. CoRR abs/1509.01770 (2015) - [i24]Yao Ma, Hao Zhang, Masashi Sugiyama:
Online Markov decision processes with policy iteration. CoRR abs/1510.04454 (2015) - [i23]Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama:
Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR abs/1511.00146 (2015) - 2014
- [j119]Takafumi Kanamori, Masashi Sugiyama:
Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. Entropy 16(2): 921-942 (2014) - [j118]Tomoya Sakai, Masashi Sugiyama:
Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. IEICE Trans. Inf. Syst. 97-D(4): 968-971 (2014) - [j117]Marthinus Christoffel du Plessis, Masashi Sugiyama:
Class Prior Estimation from Positive and Unlabeled Data. IEICE Trans. Inf. Syst. 97-D(5): 1358-1362 (2014) - [j116]Jaak Simm, Ildefons Magrans de Abril, Masashi Sugiyama:
Tree-Based Ensemble Multi-Task Learning Method for Classification and Regression. IEICE Trans. Inf. Syst. 97-D(6): 1677-1681 (2014) - [j115]Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Takafumi Kanamori, Masashi Sugiyama:
Constrained Least-Squares Density-Difference Estimation. IEICE Trans. Inf. Syst. 97-D(7): 1822-1829 (2014) - [j114]Janya Sainui, Masashi Sugiyama:
Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information. IEICE Trans. Inf. Syst. 97-D(10): 2806-2809 (2014) - [j113]Makoto Yamada, Masashi Sugiyama, Jun Sese:
Least-squares independence regression for non-linear causal inference under non-Gaussian noise. Mach. Learn. 96(3): 249-267 (2014) - [j112]Masashi Sugiyama, Gang Niu, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya:
Information-Maximization Clustering Based on Squared-Loss Mutual Information. Neural Comput. 26(1): 84-131 (2014) - [j111]Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama:
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Comput. 26(1): 185-207 (2014) - [j110]Song Liu, John A. Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi Sugiyama:
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. Neural Comput. 26(6): 1169-1197 (2014) - [j109]Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama:
Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization. Neural Comput. 26(8): 1717-1762 (2014) - [j108]Marthinus Christoffel du Plessis, Masashi Sugiyama:
Semi-supervised learning of class balance under class-prior change by distribution matching. Neural Networks 50: 110-119 (2014) - [j107]Daniele Calandriello, Gang Niu, Masashi Sugiyama:
Semi-supervised information-maximization clustering. Neural Networks 57: 103-111 (2014) - [j106]Voot Tangkaratt, Syogo Mori, Tingting Zhao, Jun Morimoto, Masashi Sugiyama:
Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation. Neural Networks 57: 128-140 (2014) - [j105]John A. Quinn, Masashi Sugiyama:
A least-squares approach to anomaly detection in static and sequential data. Pattern Recognit. Lett. 40: 36-40 (2014) - [c107]Shinichi Nakajima, Masashi Sugiyama:
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS 2014: 20-28 - [c106]Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee:
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. AISTATS 2014: 669-677 - [c105]Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto:
Efficient reuse of previous experiences in humanoid motor learning. Humanoids 2014: 554-559 - [c104]Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi:
Outlier Path: A Homotopy Algorithm for Robust SVM. ICML 2014: 1098-1106 - [c103]Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Transductive Learning with Multi-class Volume Approximation. ICML 2014: 1377-1385 - [c102]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Analysis of Learning from Positive and Unlabeled Data. NIPS 2014: 703-711 - [c101]Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi:
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. NIPS 2014: 1224-1232 - [c100]Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka:
Multitask learning meets tensor factorization: task imputation via convex optimization. NIPS 2014: 2825-2833 - [c99]Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama:
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density. ECML/PKDD (3) 2014: 19-34 - [c98]Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama:
An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions. ECML/PKDD (2) 2014: 354-369 - [i22]Toby Dylan Hocking, Supaporn Spanurattana, Masashi Sugiyama:
Support vector comparison machines. CoRR abs/1401.8008 (2014) - [i21]Gang Niu, Bo Dai, Marthinus Christoffel du Plessis, Masashi Sugiyama:
Transductive Learning with Multi-class Volume Approximation. CoRR abs/1402.0288 (2014) - [i20]Voot Tangkaratt, Ning Xie, Masashi Sugiyama:
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. CoRR abs/1404.6876 (2014) - [i19]Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto:
Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. CoRR abs/1405.2406 (2014) - 2013
- [j104]Makoto Yamada, Gordon Wichern, Kazunobu Kondo, Masashi Sugiyama, Hiroshi Sawada:
Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis. Digit. Signal Process. 23(1): 1-8 (2013) - [j103]Masashi Sugiyama:
Machine Learning with Squared-Loss Mutual Information. Entropy 15(1): 80-112 (2013) - [j102]Ildefons Magrans de Abril, Masashi Sugiyama:
Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering. IEICE Trans. Inf. Syst. 96-D(3): 742-745 (2013) - [j101]Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. IEICE Trans. Inf. Syst. 96-D(5): 1134-1144 (2013) - [j100]Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection via l1-Penalized Squared-Loss Mutual Information. IEICE Trans. Inf. Syst. 96-D(7): 1513-1524 (2013) - [j99]Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama:
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers. IEICE Trans. Inf. Syst. 96-D(8): 1871-1874 (2013) - [j98]Janya Sainui, Masashi Sugiyama:
Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering. IEICE Trans. Inf. Syst. 96-D(10): 2282-2285 (2013) - [j97]Akisato Kimura, Masashi Sugiyama, Takuho Nakano, Hirokazu Kameoka, Hitoshi Sakano, Eisaku Maeda, Katsuhiko Ishiguro:
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. Inf. Media Technol. 8(2): 311-318 (2013) - [j96]Akisato Kimura, Masashi Sugiyama, Hitoshi Sakano, Hirokazu Kameoka:
Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression. Inf. Media Technol. 8(2): 319-328 (2013) - [j95]Masao Yamanaka, Masakazu Matsugu, Masashi Sugiyama:
Salient Object Detection Based on Direct Density-ratio Estimation. Inf. Media Technol. 8(4): 929-936 (2013) - [j94]Masao Yamanaka, Masakazu Matsugu, Masashi Sugiyama:
Detection of Activities and Events without Explicit Categorization. Inf. Media Technol. 8(4): 937-943 (2013) - [j93]Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Masao Yamanaka, Makoto Yamada, Taiji Suzuki, Takafumi Kanamori:
Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. J. Comput. Sci. Eng. 7(2): 99-111 (2013) - [j92]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan, Ryota Tomioka:
Global analytic solution of fully-observed variational Bayesian matrix factorization. J. Mach. Learn. Res. 14(1): 1-37 (2013) - [j91]Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
Maximum volume clustering: a new discriminative clustering approach. J. Mach. Learn. Res. 14(1): 2641-2687 (2013) - [j90]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
Computational complexity of kernel-based density-ratio estimation: a condition number analysis. Mach. Learn. 90(3): 431-460 (2013) - [j89]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Variational Bayesian sparse additive matrix factorization. Mach. Learn. 92(2-3): 319-347 (2013) - [j88]Taiji Suzuki, Masashi Sugiyama:
Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation. Neural Comput. 25(3): 725-758 (2013) - [j87]Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput. 25(5): 1324-1370 (2013) - [j86]Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. Neural Comput. 25(6): 1512-1547 (2013) - [j85]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. Neural Comput. 25(10): 2734-2775 (2013) - [j84]Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama:
Change-point detection in time-series data by relative density-ratio estimation. Neural Networks 43: 72-83 (2013) - [c97]Masashi Sugiyama:
Divergence estimation for machine learning and signal processing. BCI 2013: 12-13 - [c96]Masashi Sugiyama:
Direct Approximation of Divergences Between Probability Distributions. Empirical Inference 2013: 273-283 - [c95]Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama:
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. ICML (3) 2013: 10-18 - [c94]Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama:
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. ICML (3) 2013: 897-905 - [c93]Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima:
Parametric Task Learning. NIPS 2013: 1358-1366 - [c92]Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi:
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering. NIPS 2013: 1439-1447 - [c91]Song Liu, John A. Quinn, Michael U. Gutmann, Masashi Sugiyama:
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. ECML/PKDD (2) 2013: 596-611 - [c90]Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances. TAAI 2013: 1-6 - [i18]Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration. CoRR abs/1301.3966 (2013) - [i17]John A. Quinn, Masashi Sugiyama:
Density Ratio Hidden Markov Models. CoRR abs/1302.3700 (2013) - [i16]Daniele Calandriello, Gang Niu, Masashi Sugiyama:
Semi-Supervised Information-Maximization Clustering. CoRR abs/1304.8020 (2013) - [i15]Marthinus Christoffel du Plessis, Masashi Sugiyama:
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances. CoRR abs/1305.0103 (2013) - [i14]Syogo Mori, Voot Tangkaratt, Tingting Zhao, Jun Morimoto, Masashi Sugiyama:
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation. CoRR abs/1307.5118 (2013) - 2012
- [b2]Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori:
Density Ratio Estimation in Machine Learning. Cambridge University Press 2012, ISBN 978-0-521-19017-6, pp. I-XII, 1-329 - [b1]Masashi Sugiyama, Motoaki Kawanabe:
Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation. Adaptive computation and machine learning, MIT Press 2012, ISBN 978-0-262-01709-1, pp. I-XIV, 1-261 - [j83]Tsubasa Kobayashi, Masashi Sugiyama:
Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier. IEICE Trans. Inf. Syst. 95-D(8): 2065-2073 (2012) - [j82]Jaak Simm, Masashi Sugiyama, Hirotaka Hachiya:
Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems. IEICE Trans. Inf. Syst. 95-D(10): 2426-2437 (2012) - [j81]Masashi Sugiyama, Makoto Yamada:
On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion. IEICE Trans. Inf. Syst. 95-D(10): 2564-2567 (2012) - [j80]Hirotaka Hachiya, Masashi Sugiyama, Naonori Ueda:
Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing 80: 93-101 (2012) - [j79]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
Statistical analysis of kernel-based least-squares density-ratio estimation. Mach. Learn. 86(3): 335-367 (2012) - [j78]Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi:
Multi-parametric solution-path algorithm for instance-weighted support vector machines. Mach. Learn. 88(3): 297-330 (2012) - [j77]Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama:
Analysis and improvement of policy gradient estimation. Neural Networks 26: 118-129 (2012) - [j76]Masayuki Karasuyama, Masashi Sugiyama:
Canonical dependency analysis based on squared-loss mutual information. Neural Networks 34: 46-55 (2012) - [j75]Nozomi Kurihara, Masashi Sugiyama:
Improving importance estimation in pool-based batch active learning for approximate linear regression. Neural Networks 36: 73-82 (2012) - [j74]Yoshinobu Kawahara, Masashi Sugiyama:
Sequential change-point detection based on direct density-ratio estimation. Stat. Anal. Data Min. 5(2): 114-127 (2012) - [j73]Masashi Sugiyama, Qiang Yang:
Introduction to the Special Section on the 2nd Asia Conference on Machine Learning (ACML 2010). ACM Trans. Intell. Syst. Technol. 3(2): 27:1 (2012) - [j72]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models. IEEE Trans. Inf. Theory 58(2): 708-720 (2012) - [c89]Hyun Ha Nam, Hirotaka Hachiya, Masashi Sugiyama:
Computationally efficient multi-label classification by least-squares probabilistic classifier. ICASSP 2012: 2077-2080 - [c88]Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama:
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. ICML 2012 - [c87]Marthinus Christoffel du Plessis, Masashi Sugiyama:
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching. ICML 2012 - [c86]Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. ICML 2012 - [c85]Akisato Kimura, Hitoshi Sakano, Hirokazu Kameoka, Masashi Sugiyama:
Designing various component analysis at will. ICPR 2012: 2959-2962 - [c84]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. NIPS 2012: 692-700 - [c83]Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. Derin Babacan:
Perfect Dimensionality Recovery by Variational Bayesian PCA. NIPS 2012: 980-988 - [c82]Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama:
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation. SSPR/SPR 2012: 363-372 - [c81]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Sparse Additive Matrix Factorization for Robust PCA and Its Generalization. ACML 2012: 301-316 - [c80]Taiji Suzuki, Masashi Sugiyama:
Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness. AISTATS 2012: 1152-1183 - [i13]Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Masashi Sugiyama:
High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso. CoRR abs/1202.0515 (2012) - [i12]Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama:
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation. CoRR abs/1203.0453 (2012) - [i11]Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Parametric Return Density Estimation for Reinforcement Learning. CoRR abs/1203.3497 (2012) - [i10]Makoto Yamada, Leonid Sigal, Michalis Raptis, Masashi Sugiyama:
Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information. CoRR abs/1206.4116 (2012) - [i9]Gang Niu, Bo Dai, Makoto Yamada, Masashi Sugiyama:
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization. CoRR abs/1206.4614 (2012) - [i8]Ning Xie, Hirotaka Hachiya, Masashi Sugiyama:
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting. CoRR abs/1206.4634 (2012) - [i7]Marthinus Christoffel du Plessis, Masashi Sugiyama:
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching. CoRR abs/1206.4677 (2012) - [i6]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. CoRR abs/1207.0099 (2012) - [i5]Akisato Kimura, Masashi Sugiyama, Hitoshi Sakano, Hirokazu Kameoka:
Designing various component analysis at will. CoRR abs/1207.3554 (2012) - [i4]Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection via L1-Penalized Squared-Loss Mutual Information. CoRR abs/1210.1960 (2012) - 2011
- [j71]Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara:
Lighting Condition Adaptation for Perceived Age Estimation. IEICE Trans. Inf. Syst. 94-D(2): 392-395 (2011) - [j70]Masashi Sugiyama, Taiji Suzuki:
Least-Squares Independence Test. IEICE Trans. Inf. Syst. 94-D(6): 1333-1336 (2011) - [j69]Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Jaak Simm:
Improving the Accuracy of Least-Squares Probabilistic Classifiers. IEICE Trans. Inf. Syst. 94-D(6): 1337-1340 (2011) - [j68]Masashi Sugiyama:
Foreword. IEICE Trans. Inf. Syst. 94-D(10): 1845 (2011) - [j67]Jaak Simm, Masashi Sugiyama, Tsuyoshi Kato:
Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers. Inf. Media Technol. 6(2): 508-515 (2011) - [j66]Jaak Simm, Masashi Sugiyama, Tsuyoshi Kato:
Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers. IPSJ Trans. Comput. Vis. Appl. 3: 1-8 (2011) - [j65]Manabu Kimura, Masashi Sugiyama:
Dependence-Maximization Clustering with Least-Squares Mutual Information. J. Adv. Comput. Intell. Intell. Informatics 15(7): 800-805 (2011) - [j64]Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama:
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. J. Mach. Learn. Res. 12: 1537-1586 (2011) - [j63]Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng:
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin. J. Mach. Learn. Res. 12: 1835-1863 (2011) - [j62]Shinichi Nakajima, Masashi Sugiyama:
Theoretical Analysis of Bayesian Matrix Factorization. J. Mach. Learn. Res. 12: 2583-2648 (2011) - [j61]Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori:
Statistical outlier detection using direct density ratio estimation. Knowl. Inf. Syst. 26(2): 309-336 (2011) - [j60]Taiji Suzuki, Masashi Sugiyama:
Least-Squares Independent Component Analysis. Neural Comput. 23(1): 284-301 (2011) - [j59]Hirotaka Hachiya, Jan Peters, Masashi Sugiyama:
Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning. Neural Comput. 23(11): 2798-2832 (2011) - [j58]Masashi Sugiyama, Makoto Yamada, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe:
Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. Neural Networks 24(2): 183-198 (2011) - [j57]Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, Manabu Kimura:
Least-squares two-sample test. Neural Networks 24(7): 735-751 (2011) - [c79]Tsuyoshi Idé, Masashi Sugiyama:
Trajectory Regression on Road Networks. AAAI 2011: 203-208 - [c78]Makoto Yamada, Masashi Sugiyama:
Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis. AAAI 2011: 549-554 - [c77]Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara, Mitsuhiro Fujita:
Multi-race age estimation based on the combination of multiple classifiers. ACPR 2011: 633-637 - [c76]Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masashi Sugiyama, Makoto Yamada, Hirokazu Kameoka:
Automatic audio tag classification via semi-supervised canonical density estimation. ICASSP 2011: 2232-2235 - [c75]Masakazu Matsugu, Masao Yamanaka, Masashi Sugiyama:
Detection of activities and events without explicit categorization. ICCV Workshops 2011: 1532-1539 - [c74]Masashi Sugiyama, Makoto Yamada, Manabu Kimura, Hirotaka Hachiya:
On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution. ICML 2011: 65-72 - [c73]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution. ICML 2011: 497-504 - [c72]Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi:
Multi-parametric solution-path algorithm for instance-weighted support vector machines. MLSP 2011: 1-6 - [c71]Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan:
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent. NIPS 2011: 208-216 - [c70]Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama:
Analysis and Improvement of Policy Gradient Estimation. NIPS 2011: 262-270 - [c69]Ichiro Takeuchi, Masashi Sugiyama:
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification. NIPS 2011: 576-584 - [c68]Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. NIPS 2011: 594-602 - [c67]Makoto Yamada, Gang Niu, Jun Takagi, Masashi Sugiyama:
Suffcient Component Analysis. ACML 2011: 247-262 - [c66]Gang Niu, Bo Dai, Lin Shang, Masashi Sugiyama:
Maximum Volume Clustering. AISTATS 2011: 561-569 - [c65]Makoto Yamada, Masashi Sugiyama:
Cross-Domain Object Matching with Model Selection. AISTATS 2011: 807-815 - [p1]Neil Rubens, Dain Kaplan, Masashi Sugiyama:
Active Learning in Recommender Systems. Recommender Systems Handbook 2011: 735-767 - 2010
- [j56]Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara:
Least-Squares Conditional Density Estimation. IEICE Trans. Inf. Syst. 93-D(3): 583-594 (2010) - [j55]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
Theoretical Analysis of Density Ratio Estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 93-A(4): 787-798 (2010) - [j54]Nobuyuki Shimizu, Masashi Sugiyama, Hiroshi Nakagawa:
Spectral Methods for Thesaurus Construction. IEICE Trans. Inf. Syst. 93-D(6): 1378-1385 (2010) - [j53]Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura:
Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation. IEICE Trans. Inf. Syst. 93-D(9): 2555-2565 (2010) - [j52]Masashi Sugiyama:
Foreword. IEICE Trans. Inf. Syst. 93-D(10): 2671 (2010) - [j51]Masashi Sugiyama:
Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting. IEICE Trans. Inf. Syst. 93-D(10): 2690-2701 (2010) - [j50]Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Jaak Simm:
Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers. IEICE Trans. Inf. Syst. 93-D(10): 2846-2849 (2010) - [j49]Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara:
A Semi-Supervised Approach to Perceived Age Prediction from Face Images. IEICE Trans. Inf. Syst. 93-D(10): 2875-2878 (2010) - [j48]Tsuyoshi Kato, Kinya Okada, Hisashi Kashima, Masashi Sugiyama:
A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference. Int. J. Knowl. Discov. Bioinform. 1(1): 66-80 (2010) - [j47]Neil Rubens, Ryota Tomioka, Masashi Sugiyama:
Output Divergence Criterion for Active Learning in Collaborative Settings. Inf. Media Technol. 5(1): 119-128 (2010) - [j46]Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese:
Semi-supervised local Fisher discriminant analysis for dimensionality reduction. Mach. Learn. 78(1-2): 35-61 (2010) - [j45]Masashi Sugiyama, Motoaki Kawanabe, Pui Ling Chui:
Dimensionality reduction for density ratio estimation in high-dimensional spaces. Neural Networks 23(1): 44-59 (2010) - [j44]Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama:
Efficient exploration through active learning for value function approximation in reinforcement learning. Neural Networks 23(5): 639-648 (2010) - [j43]Makoto Yamada, Masashi Sugiyama, Tomoko Matsui:
Semi-supervised speaker identification under covariate shift. Signal Process. 90(8): 2353-2361 (2010) - [j42]Yan Li, Hiroyuki Kambara, Yasuharu Koike, Masashi Sugiyama:
Application of Covariate Shift Adaptation Techniques in Brain-Computer Interfaces. IEEE Trans. Biomed. Eng. 57(6): 1318-1324 (2010) - [j41]Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai:
Conic Programming for Multitask Learning. IEEE Trans. Knowl. Data Eng. 22(7): 957-968 (2010) - [c64]Makoto Yamada, Masashi Sugiyama:
Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise. AAAI 2010: 643-648 - [c63]Gordon Wichern, Makoto Yamada, Harvey D. Thornburg, Masashi Sugiyama, Andreas Spanias:
Automatic audio tagging using covariate shift adaptation. ICASSP 2010: 253-256 - [c62]Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Tomoko Matsui:
Acceleration of sequence kernel computation for real-time speaker identification. ICASSP 2010: 1626-1629 - [c61]Makoto Yamada, Masashi Sugiyama, Gordon Wichern:
Direct importance estimation with probabilistic principal component analyzers. ICASSP 2010: 1962-1965 - [c60]Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Nonparametric Return Distribution Approximation for Reinforcement Learning. ICML 2010: 799-806 - [c59]Shinichi Nakajima, Masashi Sugiyama:
Implicit Regularization in Variational Bayesian Matrix Factorization. ICML 2010: 815-822 - [c58]Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi Kashima:
A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. ICML 2010: 1087-1094 - [c57]Akisato Kimura, Hirokazu Kameoka, Masashi Sugiyama, Takuho Nakano, Eisaku Maeda, Hitoshi Sakano, Katsuhiko Ishiguro:
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. ICPR 2010: 2933-2936 - [c56]Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara:
Perceived Age Estimation under Lighting Condition Change by Covariate Shift Adaptation. ICPR 2010: 3400-3403 - [c55]Shinichi Nakajima, Masashi Sugiyama, Ryota Tomioka:
Global Analytic Solution for Variational Bayesian Matrix Factorization. NIPS 2010: 1768-1776 - [c54]Hirotaka Hachiya, Masashi Sugiyama:
Feature Selection for Reinforcement Learning: Evaluating Implicit State-Reward Dependency via Conditional Mutual Information. ECML/PKDD (1) 2010: 474-489 - [c53]Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe:
Direct Density Ratio Estimation with Dimensionality Reduction. SDM 2010: 595-606 - [c52]Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, Toshiyuki Tanaka:
Parametric Return Density Estimation for Reinforcement Learning. UAI 2010: 368-375 - [c51]Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara:
Semi-supervised Estimation of Perceived Age from Face Images. VISAPP (2) 2010: 319-324 - [c50]Yasuo Tabei, Takeaki Uno, Masashi Sugiyama, Koji Tsuda:
Single versus Multiple Sorting in All Pairs Similarity Search. ACML 2010: 145-160 - [c49]Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara:
Conditional Density Estimation via Least-Squares Density Ratio Estimation. AISTATS 2010: 781-788 - [c48]Taiji Suzuki, Masashi Sugiyama:
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation. AISTATS 2010: 804-811 - [c47]Masashi Sugiyama, Qiang Yang:
Preface. ACML 2010: i-xiv - [e1]Masashi Sugiyama, Qiang Yang:
Proceedings of the 2nd Asian Conference on Machine Learning, ACML 2010, Tokyo, Japan, November 8-10, 2010. JMLR Proceedings 13, JMLR.org 2010 [contents] - [i3]Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi:
Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines. CoRR abs/1009.4791 (2010)
2000 – 2009
- 2009
- [j40]Hisashi Kashima, Yoshihiro Yamanishi, Tsuyoshi Kato, Masashi Sugiyama, Koji Tsuda:
Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. Bioinform. 25(22): 2962-2968 (2009) - [j39]Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese:
Mutual information estimation reveals global associations between stimuli and biological processes. BMC Bioinform. 10(S-1) (2009) - [j38]Masashi Sugiyama:
On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis. IEICE Trans. Inf. Syst. 92-D(5): 1204-1208 (2009) - [j37]Hisashi Kashima, Tsuyoshi Idé, Tsuyoshi Kato, Masashi Sugiyama:
Recent Advances and Trends in Large-Scale Kernel Methods. IEICE Trans. Inf. Syst. 92-D(7): 1338-1353 (2009) - [j36]Makoto Yamada, Masashi Sugiyama:
Direct Importance Estimation with Gaussian Mixture Models. IEICE Trans. Inf. Syst. 92-D(10): 2159-2162 (2009) - [j35]Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama:
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. Inf. Media Technol. 4(2): 529-546 (2009) - [j34]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang:
A Density-ratio Framework for Statistical Data Processing. Inf. Media Technol. 4(4): 962-987 (2009) - [j33]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang:
A Density-ratio Framework for Statistical Data Processing. IPSJ Trans. Comput. Vis. Appl. 1: 183-208 (2009) - [j32]Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama:
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. J. Inf. Process. 17: 138-155 (2009) - [j31]Takafumi Kanamori, Shohei Hido, Masashi Sugiyama:
A Least-squares Approach to Direct Importance Estimation. J. Mach. Learn. Res. 10: 1391-1445 (2009) - [j30]Masashi Sugiyama, Shinichi Nakajima:
Pool-based active learning in approximate linear regression. Mach. Learn. 75(3): 249-274 (2009) - [j29]Liwei Wang, Masashi Sugiyama, Cheng Yang, Kohei Hatano, Jufu Feng:
Theory and Algorithm for Learning with Dissimilarity Functions. Neural Comput. 21(5): 1459-1484 (2009) - [j28]Akiko Takeda, Masashi Sugiyama:
On Generalization Performance and Non-Convex Optimization of Extended nu-Support Vector Machine. New Gener. Comput. 27(3): 259-279 (2009) - [j27]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Adaptive importance sampling for value function approximation in off-policy reinforcement learning. Neural Networks 22(10): 1399-1410 (2009) - [j26]Ryota Tomioka, Masashi Sugiyama:
Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction. IEEE Signal Process. Lett. 16(12): 1067-1070 (2009) - [j25]Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama:
Robust Label Propagation on Multiple Networks. IEEE Trans. Neural Networks 20(1): 35-44 (2009) - [c46]Masashi Sugiyama:
Density Ratio Estimation: A New Versatile Tool for Machine Learning. ACML 2009: 6-9 - [c45]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Efficient data reuse in value function approximation. ADPRL 2009: 8-15 - [c44]Yan Li, Yasuharu Koike, Masashi Sugiyama:
A Framework of Adaptive Brain Computer Interfaces. BMEI 2009: 1-5 - [c43]Makoto Yamada, Masashi Sugiyama, Tomoko Matsui:
Covariate shift adaptation for semi-supervised speaker identification. ICASSP 2009: 1661-1664 - [c42]Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashima, Tetsuro Morimura:
Least absolute policy iteration for robust value function approximation. ICRA 2009: 2904-2909 - [c41]Taiji Suzuki, Masashi Sugiyama:
Estimating Squared-Loss Mutual Information for Independent Component Analysis. ICA 2009: 130-137 - [c40]Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama:
Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. IJCAI 2009: 980-985 - [c39]Marko V. Jankovic, Masashi Sugiyama:
Probabilistic principal component analysis based on JoyStick Probability Selector. IJCNN 2009: 1414-1421 - [c38]Taiji Suzuki, Masashi Sugiyama, Toshiyuki Tanaka:
Mutual information approximation via maximum likelihood estimation of density ratio. ISIT 2009: 463-467 - [c37]Shinichi Nakajima, Masashi Sugiyama:
Analysis of Variational Bayesian Matrix Factorization. PAKDD 2009: 314-326 - [c36]Hirotaka Hachiya, Jan Peters, Masashi Sugiyama:
Efficient Sample Reuse in EM-Based Policy Search. ECML/PKDD (1) 2009: 469-484 - [c35]Yoshinobu Kawahara, Masashi Sugiyama:
Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. SDM 2009: 389-400 - [c34]Hisashi Kashima, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, Koji Tsuda:
Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. SDM 2009: 1100-1111 - [c33]Nicole Krämer, Masashi Sugiyama, Mikio L. Braun:
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression. AISTATS 2009: 288-295 - [i2]Takeaki Uno, Masashi Sugiyama, Koji Tsuda:
Efficient Construction of Neighborhood Graphs by the Multiple Sorting Method. CoRR abs/0904.3151 (2009) - [i1]Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama:
Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation. CoRR abs/0911.4046 (2009) - 2008
- [j24]Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar:
Geodesic Gaussian kernels for value function approximation. Auton. Robots 25(3): 287-304 (2008) - [j23]Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Klaus-Robert Müller:
Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise. IEICE Trans. Inf. Syst. 91-D(5): 1577-1580 (2008) - [j22]Masashi Sugiyama, Neil Rubens:
A batch ensemble approach to active learning with model selection. Neural Networks 21(9): 1278-1286 (2008) - [j21]Marko V. Jankovic, Masashi Sugiyama:
A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule. IEEE Signal Process. Lett. 15: 677-680 (2008) - [c32]Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama, Jan Peters:
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. AAAI 2008: 1351-1356 - [c31]Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng:
On the Margin Explanation of Boosting Algorithms. COLT 2008: 479-490 - [c30]Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori:
Inlier-Based Outlier Detection via Direct Density Ratio Estimation. ICDM 2008: 223-232 - [c29]Akiko Takeda, Masashi Sugiyama:
nu-support vector machine as conditional value-at-risk minimization. ICML 2008: 1056-1063 - [c28]Neil Rubens, Vera Sheinman, Takenobu Tokunaga, Masashi Sugiyama:
Order Retrieval. LKR 2008: 310-317 - [c27]Takafumi Kanamori, Shohei Hido, Masashi Sugiyama:
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. NIPS 2008: 809-816 - [c26]Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese:
Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction. PAKDD 2008: 333-344 - [c25]Masashi Sugiyama, Shinichi Nakajima:
Pool-Based Agnostic Experiment Design in Linear Regression. ECML/PKDD (2) 2008: 406-422 - [c24]Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama:
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. SDM 2008: 443-454 - [c23]Masashi Sugiyama, Neil Rubens:
Active Learning with Model Selection in Linear Regression. SDM 2008: 518-529 - [c22]Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama:
Integration of Multiple Networks for Robust Label Propagation. SDM 2008: 716-726 - [c21]Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori:
Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. FSDM 2008: 5-20 - 2007
- [j20]Masashi Sugiyama:
Generalization Error Estimation for Non-linear Learning Methods. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 90-A(7): 1496-1499 (2007) - [j19]Yasushi Hidaka, Masashi Sugiyama:
A New Meta-Criterion for Regularized Subspace Information Criterion. IEICE Trans. Inf. Syst. 90-D(11): 1779-1786 (2007) - [j18]Shun Gokita, Masashi Sugiyama, Keisuke Sakurai:
Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 90-A(11): 2584-2592 (2007) - [j17]Masashi Sugiyama, Matthias Krauledat, Klaus-Robert Müller:
Covariate Shift Adaptation by Importance Weighted Cross Validation. J. Mach. Learn. Res. 8: 985-1005 (2007) - [j16]Masashi Sugiyama:
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. J. Mach. Learn. Res. 8: 1027-1061 (2007) - [c20]Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Müller:
Asymptotic Bayesian generalization error when training and test distributions are different. ICML 2007: 1079-1086 - [c19]Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar:
Value Function Approximation on Non-Linear Manifolds for Robot Motor Control. ICRA 2007: 1733-1740 - [c18]Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai:
Multi-Task Learning via Conic Programming. NIPS 2007: 737-744 - [c17]Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, Motoaki Kawanabe:
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. NIPS 2007: 1433-1440 - [c16]Neil Rubens, Masashi Sugiyama:
Influence-based collaborative active learning. RecSys 2007: 145-148 - 2006
- [j15]Masashi Sugiyama, Hidemitsu Ogawa:
Constructing Kernel Functions for Binary Regression. IEICE Trans. Inf. Syst. 89-D(7): 2243-2249 (2006) - [j14]Masashi Sugiyama, Keisuke Sakurai:
Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 89-A(8): 2216-2225 (2006) - [j13]Masashi Sugiyama:
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error. J. Mach. Learn. Res. 7: 141-166 (2006) - [j12]Gilles Blanchard, Motoaki Kawanabe, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller:
In Search of Non-Gaussian Components of a High-Dimensional Distribution. J. Mach. Learn. Res. 7: 247-282 (2006) - [c15]Masashi Sugiyama, Benjamin Blankertz, Matthias Krauledat, Guido Dornhege, Klaus-Robert Müller:
Importance-Weighted Cross-Validation for Covariate Shift. DAGM-Symposium 2006: 354-363 - [c14]Motoaki Kawanabe, Gilles Blanchard, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller:
A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces. ICA 2006: 149-156 - [c13]Masashi Sugiyama, Motoaki Kawanabe, Gilles Blanchard, Vladimir G. Spokoiny, Klaus-Robert Müller:
Obtaining the Best Linear Unbiased Estimator of Noisy Signals by Non-Gaussian Component Analysis. ICASSP (3) 2006: 608-611 - [c12]Masashi Sugiyama:
Local Fisher discriminant analysis for supervised dimensionality reduction. ICML 2006: 905-912 - [c11]Amos J. Storkey, Masashi Sugiyama:
Mixture Regression for Covariate Shift. NIPS 2006: 1337-1344 - [c10]Akira Tanaka, Masashi Sugiyama, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi:
Model Selection Using a Class of Kernels with an Invariant Metric. SSPR/SPR 2006: 862-870 - 2005
- [c9]Masashi Sugiyama, Klaus-Robert Müller:
Model Selection Under Covariate Shift. ICANN (2) 2005: 235-240 - [c8]Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir G. Spokoiny, Klaus-Robert Müller:
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. NIPS 2005: 131-138 - [c7]Masashi Sugiyama:
Active Learning for Misspecified Models. NIPS 2005: 1305-1312 - 2004
- [j11]Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller:
Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression. Neural Comput. 16(5): 1077-1104 (2004) - [c6]Masashi Sugiyama, Motoaki Kawanabe, Klaus-Robert Müller:
Regularizing generalization error estimators: a novel approach to robust model selection. ESANN 2004: 163-168 - [c5]Masashi Sugiyama:
Estimating the error at given test input points for linear regression. Neural Networks and Computational Intelligence 2004: 113-118 - 2003
- [j10]Masashi Sugiyama:
Improving Precision of the Subspace Information Criterion. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 86-A(7): 1885-1895 (2003) - 2002
- [j9]Masashi Sugiyama, Klaus-Robert Müller:
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces. J. Mach. Learn. Res. 3: 323-359 (2002) - [j8]Masashi Sugiyama, Hidemitsu Ogawa:
Theoretical and Experimental Evaluation of the Subspace Information Criterion. Mach. Learn. 48(1-3): 25-50 (2002) - [j7]Masashi Sugiyama, Hidemitsu Ogawa:
Optimal design of regularization term and regularization parameter by subspace information criterion. Neural Networks 15(3): 349-361 (2002) - [j6]Masashi Sugiyama, Hidemitsu Ogawa:
A unified method for optimizing linear image restoration filters. Signal Process. 82(11): 1773-1787 (2002) - [j5]Koji Tsuda, Masashi Sugiyama, Klaus-Robert Müller:
Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors. IEEE Trans. Neural Networks 13(1): 70-80 (2002) - [c4]Masashi Sugiyama, Klaus-Robert Müller:
Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces. ICANN 2002: 528-534 - 2001
- [j4]Masashi Sugiyama, Hidemitsu Ogawa:
Incremental Active Learning for Optimal Generalization. Neural Comput. 12(12): 2909-2940 (2001) - [j3]Masashi Sugiyama, Hidemitsu Ogawa:
Subspace Information Criterion for Model Selection. Neural Comput. 13(8): 1863-1889 (2001) - [j2]Masashi Sugiyama, Hidemitsu Ogawa:
Incremental projection learning for optimal generalization. Neural Networks 14(1): 53-66 (2001) - [j1]Masashi Sugiyama, Hidemitsu Ogawa:
Properties of incremental projection learning. Neural Networks 14(1): 67-78 (2001) - 2000
- [c3]Masashi Sugiyama, Hidemitsu Ogawa:
A new information criterion for the selection of subspace models. ESANN 2000: 69-74 - [c2]Masashi Sugiyama, Hidemitsu Ogawa:
Incremental Active Learning with Bias Reduction. IJCNN (1) 2000: 15-20
1990 – 1999
- 1999
- [c1]Masashi Sugiyama, Hidemitsu Ogawa:
Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks. NIPS 1999: 624-630
Coauthor Index
[j189] [c287] [c281] [i227] [i222] [i221] [j180] [c270] [c256] [i209] [i201] [i200] [i198] [i189] [j176] [j170] [c251] [c250] [c249] [c247] [c246] [c245] [c240] [c236] [i179] [i173] [c227] [c226] [c222] [c221] [c220] [c219] [c211] [i167] [i166] [i164] [i154] [i152] [i151] [i148] [c203] [c201] [c197] [c194] [c188] [c185] [c184] [i140] [i135] [i132] [i125] [i124] [i115] [i114] [i113] [i111] [i108] [j153] [c166] [c162] [i102] [i97] [i88] [i83] [i78] [i71] [c147] [c144] [i66] [i62] [i60] [i59] [i51]
[j189] [i221] [c270] [c256] [i211] [i201] [i200] [i198] [i189] [j176] [j170] [c251] [c250] [c249] [c247] [c246] [c245] [c244] [c240] [i179] [i173] [c226] [c222] [c220] [c219] [c211] [i167] [i166] [i164] [i154] [i152] [i151] [i148] [c185] [c184] [i140] [i135] [i125] [i124] [i111] [i108] [c162] [i83] [i71]
[j191] [j190] [j188] [c291] [c289] [c287] [c285] [c283] [c282] [c281] [c280] [i236] [i233] [i229] [i227] [i224] [i222] [i220] [i217] [j184] [j182] [j181] [j180] [c274] [c273] [c272] [c270] [c264] [c263] [c262] [c256] [i211] [i209] [i208] [i206] [i202] [i200] [i198] [i197] [i196] [i191] [i190] [i189] [i187] [j177] [j176] [j170] [c251] [c250] [c249] [c248] [c247] [c246] [c245] [c244] [c240] [c239] [c237] [i185] [i183] [i182] [i179] [i173] [i172] [j168] [j166] [c229] [c227] [c226] [c225] [c223] [c222] [c221] [c220] [c219] [c218] [c215] [c213] [c211] [i168] [i167] [i166] [i165] [i164] [i163] [i162] [i161] [i154] [i152] [i151] [i149] [i148] [i147] [i143] [c207] [c204] [c203] [c201] [c200] [c198] [c194] [c189] [c188] [c185] [c184] [i140] [i135] [i134] [i133] [i132] [i127] [i125] [i124] [i117] [i115] [i114] [i113] [i111] [i108] [c172] [c169] [c168] [c166] [c163] [c162] [i102] [i97] [i88] [i86] [i84] [i83] [i82] [i73] [i72] [i71] [j149] [j148] [j147] [c153] [c152] [c150] [c147] [c146] [c144] [i69] [i67] [i62] [i60] [i59] [i57] [i53] [i51] [i50] [i49] [j143] [j141] [c140] [c136] [c134] [c131] [i46] [i43] [i42] [i39] [i38] [j132] [c128] [c124] [i36] [i34] [i32] [c118] [c116] [c112] [j112] [j109] [j107] [c103] [c102] [i21] [j91] [c95] [c90] [i16] [j77] [c88] [i9] [c70] [c67] [c66]
[c261] [c253] [c252] [c243] [i180] [j167] [j165] [j164] [c233] [c228] [c218] [c212] [i168] [i150] [j158] [c202] [c196] [c195] [i137] [i136] [i131] [i126] [i123] [i118] [i107] [i105] [c180] [c179] [c178] [i101] [i93] [i92] [i89] [i87] [i81] [j150] [j145] [c159] [c157] [c152] [c151] [c145] [c142] [i70] [i68] [i61] [i56] [i55] [c135] [c133] [i45] [i31] [c101]
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