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ORCIDJun Huan
Luke Huan
Person information
- affiliation: Amazon Web Services (AWS), Seattle, WA, USA
- affiliation: StylingAI Inc, Beijing, China
- affiliation: Baidu Research, Big Data Lab, Beijing, China
- affiliation (2006 - 2018): University of Kansas, Department of Electrical Engineering and Computer Science, Lawrence, KS, USA
- affiliation (PhD 2006): University of North Carolina, Department of Computer Science, Chapel Hill, NC, USA
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2020 – today
- 2024
[j43]Siyu Huang
, Tianyang Wang, Haoyi Xiong, Bihan Wen, Jun Huan, Dejing Dou:
Temporal Output Discrepancy for Loss Estimation-Based Active Learning. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2109-2123 (2024)- [c105]Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan:
Collage: Light-Weight Low-Precision Strategy for LLM Training. ICML 2024 - [c104]Hao Ding, Ziwei Fan, Ingo Gühring, Gaurav Gupta, Wooseok Ha, Jun Huan, Linbo Liu, Behrooz Omidvar-Tehrani, Shiqi Wang, Hao Zhou:
Reasoning and Planning with Large Language Models in Code Development. KDD 2024: 6480-6490 - [c103]Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis:
Inference Optimization of Foundation Models on AI Accelerators. KDD 2024: 6605-6615 - [c102]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka:
The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. KDD 2024: 6733-6734 - [c101]Ye Xing, Jun Huan, Wee Hyong Tok, Cong Shen, Johannes Gehrke, Katherine Lin, Arjun Guha, Omer Tripp, Murali Krishna Ramanathan:
NL2Code-Reasoning and Planning with LLMs for Code Development. KDD 2024: 6745-6746 - [i27]Haozheng Fan, Hao Zhou, Guangtai Huang, Parameswaran Raman, Xinwei Fu, Gaurav Gupta, Dhananjay Ram, Yida Wang, Jun Huan:
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium. CoRR abs/2404.10630 (2024) - [i26]Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan:
Collage: Light-Weight Low-Precision Strategy for LLM Training. CoRR abs/2405.03637 (2024) - [i25]Youngsuk Park, Kailash Budhathoki, Liangfu Chen, Jonas M. Kübler, Jiaji Huang, Matthäus Kleindessner, Jun Huan, Volkan Cevher, Yida Wang, George Karypis:
Inference Optimization of Foundation Models on AI Accelerators. CoRR abs/2407.09111 (2024) - 2023
- [j42]Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiong Bill Yu, Jun Huan, Xiao Liu, Xiang Zhang:
Random Walk on Multiple Networks. IEEE Trans. Knowl. Data Eng. 35(8): 8417-8430 (2023) - [c100]Charles Marx, Youngsuk Park, Hilaf Hasson, Yuyang Wang, Stefano Ermon, Luke Huan:
But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI. AISTATS 2023: 7375-7391 - [c99]Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan:
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. ICLR 2023 - [c98]Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, Jun Huan:
Fast Federated Machine Unlearning with Nonlinear Functional Theory. ICML 2023: 4241-4268 - [c97]Aashiq Muhamed, Christian Bock, Rahul Solanki, Youngsuk Park, Yida Wang, Jun Huan:
Training Large-scale Foundation Models on Emerging AI Chips. KDD 2023: 5821-5822 - [c96]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Yuriy Nevmyvaka:
The 9th SIGKDD International Workshop on Mining and Learning from Time Series. KDD 2023: 5876-5877 - [i24]Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiong Bill Yu, Jun Huan, Xiao Liu, Xiang Zhang:
Random Walk on Multiple Networks. CoRR abs/2307.01637 (2023) - 2022
- [j41]Hao Zhang, Shuigeng Zhou, Chuanxu Yan, Jihong Guan, Xin Wang, Ji Zhang, Jun Huan:
Learning Causal Structures Based on Divide and Conquer. IEEE Trans. Cybern. 52(5): 3232-3243 (2022) - [j40]Xingjian Li, Haoyi Xiong, Zeyu Chen, Jun Huan, Ji Liu, Cheng-Zhong Xu, Dejing Dou:
Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks. ACM Trans. Knowl. Discov. Data 16(3): 42:1-42:20 (2022) - [j39]Haoyi Xiong, Ruosi Wan, Jian Zhao, Zeyu Chen, Xingjian Li, Zhanxing Zhu, Jun Huan:
GrOD: Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training. ACM Trans. Knowl. Discov. Data 16(6): 117:1-117:25 (2022) - [c95]Siyu Huang, Haoyi Xiong, Tianyang Wang, Bihan Wen, Qingzhong Wang, Zeyu Chen, Jun Huan, Dejing Dou:
Parameter-Free Style Projection for Arbitrary Image Style Transfer. ICASSP 2022: 2070-2074 - [c94]Patrick Koch, Brett Wujek, Jun Liu, Jun Huan, Tao Wang:
The Sixth International Workshop on Automation in Machine Learning. KDD 2022: 4880-4881 - [c93]Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka:
8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. KDD 2022: 4896-4897 - [i23]Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan:
Towards Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. CoRR abs/2207.09572 (2022) - [i22]Siyu Huang, Tianyang Wang, Haoyi Xiong, Bihan Wen, Jun Huan, Dejing Dou:
Temporal Output Discrepancy for Loss Estimation-based Active Learning. CoRR abs/2212.10613 (2022) - 2021
- [j38]Xingjian Li, Haoyi Xiong, Zeyu Chen, Jun Huan, Cheng-Zhong Xu, Dejing Dou:
"In-Network Ensemble": Deep Ensemble Learning with Diversified Knowledge Distillation. ACM Trans. Intell. Syst. Technol. 12(5): 63:1-63:19 (2021) - [j37]Kafeng Wang, Haoyi Xiong, Jiang Bian, Zhanxing Zhu, Qian Gao, Zhishan Guo, Cheng-Zhong Xu, Jun Huan, Dejing Dou:
Sampling Sparse Representations with Randomized Measurement Langevin Dynamics. ACM Trans. Knowl. Discov. Data 15(2): 21:1-21:21 (2021) - [j36]Xiaoyang Chen, Hongwei Huo, Jun Huan, Jeffrey Scott Vitter, Weiguo Zheng, Lei Zou:
MSQ-Index: A Succinct Index for Fast Graph Similarity Search. IEEE Trans. Knowl. Data Eng. 33(6): 2654-2668 (2021) - [c92]Siyu Huang, Tianyang Wang, Haoyi Xiong, Jun Huan, Dejing Dou:
Semi-Supervised Active Learning with Temporal Output Discrepancy. ICCV 2021: 3427-3436 - [i21]Siyu Huang, Tianyang Wang, Haoyi Xiong, Jun Huan, Dejing Dou:
Semi-Supervised Active Learning with Temporal Output Discrepancy. CoRR abs/2107.14153 (2021) - 2020
- [j35]Zhigang Sun, Hongwei Huo, Jun Huan, Jeffrey Scott Vitter:
Feature reduction based on semantic similarity for graph classification. Neurocomputing 397: 114-126 (2020) - [j34]Chao Lan, Sai Nivedita Chandrasekaran, Jun Huan:
On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics. IEEE ACM Trans. Comput. Biol. Bioinform. 17(4): 1352-1363 (2020) - [j33]Jiang Bian, Haoyi Xiong, Yanjie Fu, Jun Huan, Zhishan Guo:
MP2SDA: Multi-Party Parallelized Sparse Discriminant Learning. ACM Trans. Knowl. Discov. Data 14(3): 26:1-26:22 (2020) - [c91]Jie An, Haoyi Xiong, Jun Huan, Jiebo Luo:
Ultrafast Photorealistic Style Transfer via Neural Architecture Search. AAAI 2020: 10443-10450 - [c90]Sohaib Kiani, Sana Awan, Jun Huan, Fengjun Li, Bo Luo:
WOLF: automated machine learning workflow management framework for malware detection and other applications. HotSoS 2020: 11:1-11:8 - [c89]Yuchen Bian, Jun Huan, Dejing Dou, Xiang Zhang:
Rethinking Local Community Detection: Query Nodes Replacement. ICDM 2020: 930-935 - [c88]Yingzhen Yang, Jiahui Yu, Nebojsa Jojic, Jun Huan, Thomas S. Huang:
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary. ICLR 2020 - [c87]Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu:
On the Noisy Gradient Descent that Generalizes as SGD. ICML 2020: 10367-10376 - [c86]Siyu Huang, Haoyi Xiong, Zhi-Qi Cheng, Qingzhong Wang, Xingran Zhou, Bihan Wen, Jun Huan, Dejing Dou:
Generating Person Images with Appearance-aware Pose Stylizer. IJCAI 2020: 623-629 - [c85]Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiao Liu, Jun Huan, Xiang Zhang:
Local Community Detection in Multiple Networks. KDD 2020: 266-274 - [i20]Siyu Huang, Haoyi Xiong, Tianyang Wang, Qingzhong Wang, Zeyu Chen, Jun Huan, Dejing Dou:
Parameter-Free Style Projection for Arbitrary Style Transfer. CoRR abs/2003.07694 (2020) - [i19]Siyu Huang, Haoyi Xiong, Zhi-Qi Cheng, Qingzhong Wang, Xingran Zhou, Bihan Wen, Jun Huan, Dejing Dou:
Generating Person Images with Appearance-aware Pose Stylizer. CoRR abs/2007.09077 (2020) - [i18]Dongsheng Luo, Yuchen Bian, Xiang Zhang, Jun Huan:
Attentive Social Recommendation: Towards User And Item Diversities. CoRR abs/2011.04797 (2020)
2010 – 2019
- 2019
- [j32]Qiang Zhou, Wen'an Zhou, Bin Yang, Jun Huan:
Deep cycle autoencoder for unsupervised domain adaptation with generative adversarial networks. IET Comput. Vis. 13(7): 659-665 (2019) - [j31]Xiaoyang Chen, Hongwei Huo, Jun Huan, Jeffrey Scott Vitter:
An efficient algorithm for graph edit distance computation. Knowl. Based Syst. 163: 762-775 (2019) - [j30]Hao Zhang, Shuigeng Zhou, Jihong Guan, Jun Huan:
Measuring Conditional Independence by Independent Residuals for Causal Discovery. ACM Trans. Intell. Syst. Technol. 10(5): 50:1-50:19 (2019) - [c84]Haoyi Xiong, Kafeng Wang, Jiang Bian, Zhanxing Zhu, Cheng-Zhong Xu, Zhishan Guo, Jun Huan:
SpHMC: Spectral Hamiltonian Monte Carlo. AAAI 2019: 5516-5524 - [c83]Hannah Kim, Denys Katerenchuk, Daniel Billet, Jun Huan, Haesun Park, Boyang Li:
Understanding Actors and Evaluating Personae with Gaussian Embeddings. AAAI 2019: 6570-6577 - [c82]Zhi Feng, Jun Huan, Haoyi Xiong, Chuanyuan Song, Sijia Yang, Baoxin Zhao, Licheng Wang, Zeyu Chen, Shengwen Yang, Liping Liu:
SecureGBM: Secure Multi-Party Gradient Boosting. IEEE BigData 2019: 1312-1321 - [c81]Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan:
Towards Making Deep Transfer Learning Never Hurt. ICDM 2019: 578-587 - [c80]Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, Jun Huan:
Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks. ICLR (Poster) 2019 - [c79]Tianyang Wang, Jun Huan, Bo Li, Kaoning Hu:
Rethink Gaussian Denoising Prior for Real-World Image Denoising. ICTAI 2019: 1664-1668 - [c78]Fan Wu, Shuigeng Zhou, Kang Wang, Yi Xu, Jihong Guan, Jun Huan:
Simple Is Better: A Global Semantic Consistency Based End-to-End Framework for Effective Zero-Shot Learning. PRICAI (1) 2019: 98-112 - [c77]Tianyang Wang, Jun Huan, Michelle Zhu:
Instance-Based Deep Transfer Learning. WACV 2019: 367-375 - [e6]Chaitanya K. Baru, Jun Huan, Latifur Khan, Xiaohua Hu, Ronay Ak, Yuanyuan Tian, Roger S. Barga, Carlo Zaniolo, Kisung Lee, Yanfang (Fanny) Ye:
2019 IEEE International Conference on Big Data (IEEE BigData), Los Angeles, CA, USA, December 9-12, 2019. IEEE 2019, ISBN 978-1-7281-0858-2 [contents] - [i17]Wenqing Hu, Zhanxing Zhu, Haoyi Xiong, Jun Huan:
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent. CoRR abs/1901.06054 (2019) - [i16]Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, Jun Huan:
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks. CoRR abs/1901.09229 (2019) - [i15]Yingzhen Yang, Xingjian Li, Jun Huan:
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity. CoRR abs/1902.00873 (2019) - [i14]Yingzhen Yang, Nebojsa Jojic, Jun Huan:
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary. CoRR abs/1902.03264 (2019) - [i13]Jie An, Haoyi Xiong, Jinwen Ma, Jiebo Luo, Jun Huan:
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks. CoRR abs/1906.02470 (2019) - [i12]Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Zhanxing Zhu:
The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. CoRR abs/1906.07405 (2019) - [i11]Hanchao Wang, Jun Huan:
AGAN: Towards Automated Design of Generative Adversarial Networks. CoRR abs/1906.11080 (2019) - [i10]Jie An, Haoyi Xiong, Jiebo Luo, Jun Huan, Jinwen Ma:
Fast Universal Style Transfer for Artistic and Photorealistic Rendering. CoRR abs/1907.03118 (2019) - [i9]Isaac Ahern, Adam Noack, Luis Guzman-Nateras, Dejing Dou, Boyang Li, Jun Huan:
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks. CoRR abs/1909.04200 (2019) - [i8]Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, Jun Huan:
Towards Making Deep Transfer Learning Never Hurt. CoRR abs/1911.07489 (2019) - [i7]Zhi Feng, Haoyi Xiong, Chuanyuan Song, Sijia Yang, Baoxin Zhao, Licheng Wang, Zeyu Chen, Shengwen Yang, Liping Liu, Jun Huan:
SecureGBM: Secure Multi-Party Gradient Boosting. CoRR abs/1911.11997 (2019) - [i6]Jie An, Haoyi Xiong, Jun Huan, Jiebo Luo:
Ultrafast Photorealistic Style Transfer via Neural Architecture Search. CoRR abs/1912.02398 (2019) - 2018
- [c76]Xiaoli Li, Jun Huan:
Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity. CIKM 2018: 853-861 - [c75]Tianyang Wang, Jun Huan, Bo Li:
Data Dropout: Optimizing Training Data for Convolutional Neural Networks. ICTAI 2018: 39-46 - [r2]Jun Huan:
Frequent Graph Patterns. Encyclopedia of Database Systems (2nd ed.) 2018 - [i5]Tianyang Wang, Jun Huan, Bo Li:
Data Dropout: Optimizing Training Data for Convolutional Neural Networks. CoRR abs/1809.00193 (2018) - [i4]Tianyang Wang, Jun Huan, Michelle Zhu:
Instance-based Deep Transfer Learning. CoRR abs/1809.02776 (2018) - 2017
- [j29]Xiaoyang Chen, Hongwei Huo, Jun Huan, Jeffrey Scott Vitter:
Efficient Graph Similarity Search in External Memory. IEEE Access 5: 4551-4560 (2017) - [j28]Alexios Koutsoukas, Keith J. Monaghan, Xiaoli Li, Jun Huan:
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. J. Cheminformatics 9(1): 42:1-42:13 (2017) - [j27]Chao Lan, Yuhao Yang, Xiaoli Li, Bo Luo, Jun Huan:
Learning Social Circles in Ego-Networks Based on Multi-View Network Structure. IEEE Trans. Knowl. Data Eng. 29(8): 1681-1694 (2017) - [c74]Xiaoli Li, Sai Nivedita Chandrasekaran, Jun Huan:
Lifelong multi-task multi-view learning using latent spaces. IEEE BigData 2017: 37-46 - [c73]Xiaoli Li, Jun Huan:
Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics. KDD 2017: 285-294 - [c72]Joseph St. Amand, Jun Huan:
Sparse Compositional Local Metric Learning. KDD 2017: 1097-1104 - [i3]Chao Lan, Jun Huan:
Discriminatory Transfer. CoRR abs/1707.00780 (2017) - [i2]Xiaoyang Chen, Hongwei Huo, Jun Huan, Jeffrey Scott Vitter:
Fast Computation of Graph Edit Distance. CoRR abs/1709.10305 (2017) - 2016
- [j26]Jingshan Huang, Karen Eilbeck, Barry Smith, Judith A. Blake, Dejing Dou, Weili Huang, Darren A. Natale, Alan Ruttenberg, Jun Huan, Michael T. Zimmermann, Guoqian Jiang, Yu Lin, Bin Wu, Harrison J. Strachan, Yongqun He, Shaojie Zhang, Xiaowei Wang, Zixing Liu, Glen M. Borchert, Ming Tan:
The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology. J. Biomed. Semant. 7: 24 (2016) - [j25]Qiang Yu, Hongwei Huo, Ruixing Zhao, Dazheng Feng, Jeffrey Scott Vitter, Jun Huan:
RefSelect: a reference sequence selection algorithm for planted (l, d) motif search. BMC Bioinform. 17(S-9): 266 (2016) - [j24]Jingshan Huang, Karen Eilbeck, Barry Smith, Judith A. Blake, Dejing Dou, Weili Huang, Darren A. Natale, Alan Ruttenberg, Jun Huan, Michael T. Zimmermann, Guoqian Jiang, Yu Lin, Bin Wu, Harrison J. Strachan, Nisansa de Silva, Mohan Vamsi Kasukurthi, Vikash Kumar Jha, Yongqun He, Shaojie Zhang, Xiaowei Wang, Zixing Liu, Glen M. Borchert, Ming Tan:
The development of non-coding RNA ontology. Int. J. Data Min. Bioinform. 15(3): 214-232 (2016) - [c71]Sai Nivedita Chandrasekaran, Alexios Koutsoukas, Jun Huan:
Investigating Multiview and Multitask Learning Frameworks for Predicting Drug-Disease Associations. BCB 2016: 138-145 - [c70]Jun Huan:
Deep-Learning: Investigating feed-forward deep Neural Networks for modeling high throughput chemical bioactivity data. BIBM 2016: 5 - [c69]Chao Lan, Sai Nivedita Chandrasekaran, Jun Huan:
Learning with Positive and Unknown Features. BIBM 2016: 613-618 - [c68]Sai Nivedita Chandrasekaran, Jun Huan:
Weighted multiview learning for predicting drug-disease associations. BIBM 2016: 699-702 - [c67]Chao Lan, Sai Nivedita Chandrasekaran, Jun Huan:
A distributed and privatized framework for drug-target interaction prediction. BIBM 2016: 731-734 - [c66]Xiaoli Li, Jun Huan:
aptMTVL: Nailing Interactions in Multi-Task Multi-View Multi-Label Learning using Adaptive-basis Multilinear Factor Analyzers. CIKM 2016: 1171-1180 - [c65]Joseph St. Amand, Jun Huan:
Discriminative View Learning for Single View Co-Training. CIKM 2016: 2221-2226 - [c64]Hongwei Huo, Zhigang Sun, Shuangjiang Li, Jeffrey Scott Vitter, Xinkun Wang, Qiang Yu, Jun Huan:
CS2A: A Compressed Suffix Array-Based Method for Short Read Alignment. DCC 2016: 271-278 - [c63]Gowtham Kumar Golla, Jordan A. Carlson, Jun Huan, Jacqueline Kerr, Tarrah Mitchell, Kelsey Borner:
Developing Novel Machine Learning Algorithms to Improve Sedentary Assessment for Youth Health Enhancement. ICHI 2016: 375-379 - [c62]Chao Lan, Xiaoli Li, Yujie Deng, Joseph St. Amand, Jun Huan:
A PAC bound for joint matrix completion based on Partially Collective Matrix Factorization. ICPR 2016: 2628-2633 - [c61]Chao Lan, Yujie Deng, Xiaoli Li, Jun Huan:
Co-regularized least square regression for multi-view multi-class classification. IJCNN 2016: 342-347 - [c60]Chao Lan, Yujie Deng, Jun Huan:
A disagreement-based active matrix completion approach with provable guarantee. IJCNN 2016: 4082-4088 - [c59]Chao Lan, Xiaoli Li, Yujie Deng, Jun Huan:
Partial Collective Matrix Factorization and its PAC Bound. ISAIM 2016 - [c58]Chao Lan, Jianxin Wang, Jun Huan:
Towards a Theoretical Understanding of Negative Transfer in Collective Matrix Factorization. UAI 2016 - [i1]Xiaoyang Chen, Hongwei Huo, Jun Huan, Jeffrey Scott Vitter:
MSQ-Index: A Succinct Index for Fast Graph Similarity Search. CoRR abs/1612.09155 (2016) - 2015
- [j23]Qiang Yu, Hongwei Huo, Jeffrey Scott Vitter, Jun Huan, Yakov Nekrich:
An Efficient Exact Algorithm for the Motif Stem Search Problem over Large Alphabets. IEEE ACM Trans. Comput. Biol. Bioinform. 12(2): 384-397 (2015) - [c57]Qiang Yu, Hongwei Huo, Ruixing Zhao, Dazheng Feng, Jeffrey Scott Vitter, Jun Huan:
Reference sequence selection for motif searches. BIBM 2015: 569-574 - [c56]Meenakshi Mishra, Jun Huan:
Learning Task Grouping using Supervised Task Space Partitioning in Lifelong Multitask Learning. CIKM 2015: 1091-1100 - [c55]Chao Lan, Jun Huan:
Reducing the Unlabeled Sample Complexity of Semi-Supervised Multi-View Learning. KDD 2015: 627-634 - [e5]Jun Huan, Satoru Miyano, Amarda Shehu, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Vijay K. Gombar, Matthieu-P. Schapranow, Illhoi Yoo, Jiayu Zhou, Brian Chen, Vinay Pai, Brian G. Pierce:
2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, DC, USA, November 9-12, 2015. IEEE Computer Society 2015, ISBN 978-1-4673-6799-8 [contents] - 2014
- [j22]Hongliang Fei, Jun Huan:
Structured Sparse Boosting for Graph Classification. ACM Trans. Knowl. Discov. Data 9(1): 4:1-4:22 (2014) - [c54]Qiang Yu, Hongwei Huo, Xiaoyang Chen, Haitao Guo, Jeffrey Scott Vitter, Jun Huan:
An efficient motif finding algorithm for large DNA data sets. BIBM 2014: 397-402 - [c53]Yuhao Yang, Chao Lan, Xiaoli Li, Bo Luo, Jun Huan:
Automatic Social Circle Detection Using Multi-View Clustering. CIKM 2014: 1019-1028 - [e4]Jimmy Lin, Jian Pei, Xiaohua Hu, Wo Chang, Raghunath Nambiar, Charu C. Aggarwal, Nick Cercone, Vasant G. Honavar, Jun Huan, Bamshad Mobasher, Saumyadipta Pyne:
2014 IEEE International Conference on Big Data (IEEE BigData 2014), Washington, DC, USA, October 27-30, 2014. IEEE Computer Society 2014, ISBN 978-1-4799-5665-4 [contents] - 2013
- [j21]Meenakshi Mishra, Hongliang Fei, Jun Huan:
Computational prediction of toxicity. Int. J. Data Min. Bioinform. 8(3): 338-348 (2013) - [j20]Hongliang Fei, Jun Huan:
Structured feature selection and task relationship inference for multi-task learning. Knowl. Inf. Syst. 35(2): 345-364 (2013) - [j19]Said Bleik, Meenakshi Mishra, Jun Huan, Min Song:
Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary. IEEE ACM Trans. Comput. Biol. Bioinform. 10(5): 1211-1217 (2013) - [j18]Ruoyi Jiang, Hongliang Fei, Jun Huan:
A Family of Joint Sparse PCA Algorithms for Anomaly Localization in Network Data Streams. IEEE Trans. Knowl. Data Eng. 25(11): 2421-2433 (2013) - [c52]Peng Hao, Jintao Zhang, Jun Huan:
A new on-line chemical biology data visualization system. BIBM 2013: 35-37 - [c51]Qiang Yu, Hongwei Huo, Jeffrey Scott Vitter, Jun Huan, Yakov Nekrich:
StemFinder: An efficient algorithm for searching motif stems over large alphabets. BIBM 2013: 473-476 - [c50]Jingshan Huang, Jun Huan, Alexander Tropsha, Jiangbo Dang, He Zhang, Min Xiong:
Semantics-driven frequent data pattern mining on electronic health records for effective adverse drug event monitoring. BIBM 2013: 608-611 - [c49]Aaron Smalter Hall, Jun Huan:
KUChemBio: A repository of computational chemical biology data sets. IEEE BigData 2013: 37-42 - [c48]Meenakshi Mishra, Jun Huan:
Multitask Learning with Feature Selection for Groups of Related Tasks. ICDM 2013: 1157-1162 - 2012
- [j17]Mohammad Al Hasan, Jun Huan, Jake Yue Chen, Mohammed J. Zaki:
Biological knowledge discovery and data mining. Sci. Program. 20(1): 1-2 (2012) - [j16]Brian Quanz, Jun Huan, Meenakshi Mishra:
Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue. IEEE Trans. Knowl. Data Eng. 24(10): 1789-1802 (2012) - [c47]Jintao Zhang, Gerald H. Lushington, Jun Huan:
Multi-target protein-chemical interaction prediction using task-regularized and boosted multi-task learning. BCB 2012: 60-67 - [c46]Jintao Zhang, Jun Huan:
Drug-induced QT prolongation prediction using co-regularized multi-view learning. BIBM 2012: 1-6 - [c45]Avindra Fernando, Jun Huan, Justin P. Blumenstiel, Jin Lin, Xue-wen Chen, Bo Luo:
Identification of transposable elements of the giant panda (Ailuropoda melanoleuca) genome. BIBM Workshops 2012: 674-681 - [c44]Yi Jia, Wenrong Zeng, Jun Huan:
Non-stationary bayesian networks based on perfect simulation. CIKM 2012: 1095-1104 - [c43]Brian Quanz, Jun Huan:
CoNet: feature generation for multi-view semi-supervised learning with partially observed views. CIKM 2012: 1273-1282 - [c42]Xin Huang, Hong Cheng, Jiong Yang, Jeffrey Xu Yu, Hongliang Fei, Jun Huan:
Semi-supervised Clustering of Graph Objects: A Subgraph Mining Approach. DASFAA (1) 2012: 197-212 - [c41]Brian Quanz, Jun Huan:
When Additional Views are Not Free: Active View Completion for Multi-view Semi-Supervised Learning. ICDM Workshops 2012: 169-178 - [c40]Meenakshi Mishra, Jun Huan, Said Bleik, Min Song:
Biomedical text categorization with concept graph representations using a controlled vocabulary. BIOKDD 2012: 26-32 - [c39]Jintao Zhang, Jun Huan:
Inductive multi-task learning with multiple view data. KDD 2012: 543-551 - 2011
- [j15]Jintao Zhang, Gerald H. Lushington, Jun Huan:
The BioAssay network and its implications to future therapeutic discovery. BMC Bioinform. 12(S-5): S1 (2011) - [j14]Jintao Zhang, Gerald H. Lushington, Jun Huan:
Characterizing the Diversity and Biological Relevance of the MLPCN Assay Manifold and Screening Set. J. Chem. Inf. Model. 51(6): 1205-1215 (2011) - [j13]Yi Jia, Jintao Zhang, Jun Huan:
An efficient graph-mining method for complicated and noisy data with real-world applications. Knowl. Inf. Syst. 28(2): 423-447 (2011) - [j12]Fang-Xiang Wu, Jun Huan:
Guest Editorial: Special Focus on Bioinformatics and Systems Biology. IEEE ACM Trans. Comput. Biol. Bioinform. 8(2): 292-293 (2011) - [c38]Aaron Smalter Hall, Jun Huan, Gerald H. Lushington:
Similarity boosting for label noise tolerance in protein-chemical interaction prediction. BCB 2011: 226-234 - [c37]Meenakshi Mishra, Brian Potetz, Jun Huan:
Bayesian Classifiers for Chemical Toxicity Prediction. BIBM 2011: 595-599 - [c36]Hongliang Fei, Ruoyi Jiang, Yuhao Yang, Bo Luo, Jun Huan:
Content based social behavior prediction: a multi-task learning approach. CIKM 2011: 995-1000 - [c35]Brian Quanz, Jun Huan, Meenakshi Mishra:
Knowledge transfer with low-quality data: A feature extraction issue. ICDE 2011: 769-779 - [c34]Hongliang Fei, Jun Huan:
Structured Feature Selection and Task Relationship Inference for Multi-task Learning. ICDM 2011: 171-180 - [c33]Ruoyi Jiang, Hongliang Fei, Jun Huan:
Anomaly localization for network data streams with graph joint sparse PCA. KDD 2011: 886-894 - [p1]Xiaohong Wang, Jun Huan:
G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases. Graph Data Management 2011: 176-213 - [e3]Mohammed J. Zaki, Jake Yue Chen, Mohammad Al Hasan, Jun Huan:
Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics, BIOKDD '11, San Diego, California, USA, August 21, 2011. ACM 2011, ISBN 978-1-4503-0839-7 [contents] - 2010
- [j11]Xiaohong Wang, Jun Huan, Aaron M. Smalter, Gerald H. Lushington:
Application of kernel functions for accurate similarity search in large chemical databases. BMC Bioinform. 11(S-3): 8 (2010) - [j10]Yi Jia, Jun Huan:
Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism. BMC Bioinform. 11(S-6): S27 (2010) - [j9]Seak Fei Lei, Jun Huan:
Towards site-based protein functional annotations. Int. J. Data Min. Bioinform. 4(4): 452-470 (2010) - [j8]Aaron M. Smalter, Jun Huan, Yi Jia, Gerald H. Lushington:
GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics. IEEE ACM Trans. Comput. Biol. Bioinform. 7(2): 197-207 (2010) - [j7]Deepak Bandyopadhyay, Jun Huan, Jinze Liu, Jan F. Prins, Jack Snoeyink, Wei Wang, Alexander Tropsha:
Functional neighbors: inferring relationships between nonhomologous protein families using family-specific packing motifs. IEEE Trans. Inf. Technol. Biomed. 14(5): 1137-1143 (2010) - [c32]Jintao Zhang, Gerald H. Lushington, Jun Huan:
Exploratory analysis of the BioAssay Network with implications to therapeutic discovery. BIBM 2010: 569-572 - [c31]Meenakshi Mishra, Hongliang Fei, Jun Huan:
Computational prediction of toxicity. BIBM 2010: 686-691 - [c30]Hongliang Fei, Brian Quanz, Jun Huan:
Regularization and feature selection for networked features. CIKM 2010: 1893-1896 - [c29]Jun Huan:
Knowledge Discovery in Academic Drug Discovery Programs: Opportunities and Challenges. ICDM 2010: 1218 - [c28]Jintao Zhang, Jun Huan:
Novel biological network features discovery for in silico identification of drug targets. IHI 2010: 144-152 - [c27]Hongliang Fei, Jun Huan:
Boosting with structure information in the functional space: an application to graph classification. KDD 2010: 643-652
2000 – 2009
- 2009
- [j6]Yi Jia, Jun Huan, Vincent Buhr, Jintao Zhang, Leonidas N. Carayannopoulos:
Towards comprehensive structural motif mining for better fold annotation in the "twilight zone" of sequence dissimilarity. BMC Bioinform. 10(S-1) (2009) - [j5]Aaron M. Smalter, Jun Huan, Gerald H. Lushington:
Graph Wavelet Alignment Kernels for Drug Virtual Screening. J. Bioinform. Comput. Biol. 7(3): 473-497 (2009) - [j4]Deepak Bandyopadhyay, Jun Huan, Jan F. Prins, Jack Snoeyink, Wei Wang, Alexander Tropsha:
Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development. J. Comput. Aided Mol. Des. 23(11): 773-784 (2009) - [j3]Deepak Bandyopadhyay, Jun Huan, Jan F. Prins, Jack Snoeyink, Wei Wang, Alexander Tropsha:
Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications. J. Comput. Aided Mol. Des. 23(11): 785-797 (2009) - [c26]Yi Jia, Jun Huan:
The Analysis of Arabidopsis thaliana Circadian Network Based on Non-stationary DBNs Approach with Flexible Time Lag Choosing Mechanism. BIBM 2009: 178-181 - [c25]Xiaohong Wang, Jun Huan, Aaron M. Smalter, Gerald H. Lushington:
Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases. BIBM 2009: 356-361 - [c24]Hongliang Fei, Jun Huan:
L2 norm regularized feature kernel regression for graph data. CIKM 2009: 593-600 - [c23]Brian Quanz, Jun Huan:
Large margin transductive transfer learning. CIKM 2009: 1327-1336 - [c22]Xiaohong Wang, Aaron M. Smalter, Jun Huan, Gerald H. Lushington:
G-hash: towards fast kernel-based similarity search in large graph databases. EDBT 2009: 472-480 - [c21]Brian Quanz, Hongliang Fei, Jun Huan, Joseph B. Evans, Victor Frost, Gary J. Minden, Daniel D. Deavours, Leon S. Searl, Daniel DePardo, Martin Kuehnhausen, Daniel Fokum, Matt Zeets, Angela Oguna:
Anomaly Detection with Sensor Data for Distributed Security. ICCCN 2009: 1-6 - [c20]Hongliang Fei, Brian Quanz, Jun Huan:
GLSVM: Integrating Structured Feature Selection and Large Margin Classification. ICDM Workshops 2009: 362-367 - [c19]Aaron M. Smalter, Jun Huan, Gerald H. Lushington:
Feature Selection in the Tensor Product Feature Space. ICDM 2009: 1004-1009 - [c18]Brian Quanz, Jun Huan:
Aligned Graph Classification with Regularized Logistic Regression. SDM 2009: 353-364 - [e2]Doheon Lee, Russ B. Altman, Min Song, Jun Huan:
Proceeding of the 3rd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO 2009, Hong Kong, China, November 6, 2009. ACM 2009, ISBN 978-1-60558-803-2 [contents] - [r1]Jun Huan:
Frequent Graph Patterns. Encyclopedia of Database Systems 2009: 1170-1175 - 2008
- [c17]Aaron M. Smalter, Jun Huan, Gerald H. Lushington:
Chemical Compound Classification with Automatically Mined Structure Patterns. APBC 2008: 39-48 - [c16]Hongliang Fei, Jun Huan:
Structure feature selection for chemical compound classification. BIBE 2008: 1-6 - [c15]Aaron M. Smalter, Jun Huan, Gerald H. Lushington:
GPM: A graph pattern matching kernel with diffusion for chemical compound classification. BIBE 2008: 1-6 - [c14]Seak Fei Lei, Jun Huan:
Towards Site-Based Protein Functional Annotations. BIBM 2008: 193-198 - [c13]Deepak Bandyopadhyay, Jun Huan, Jinze Liu, Jan F. Prins, Jack Snoeyink, Wei Wang, Alexander Tropsha:
Functional Neighbors: Inferring Relationships between Non-Homologous Protein Families Using Family-Specific Packing Motifs. BIBM 2008: 199-206 - [c12]Brian Quanz, Meeyoung Park, Jun Huan:
Biological pathways as features for microarray data classification. DTMBIO 2008: 5-12 - [c11]Hongliang Fei, Jun Huan:
Structure feature selection for graph classification. CIKM 2008: 991-1000 - [e1]Min Song, Doheon Kim, Luke Huan:
Proceeding of the 2nd International Workshop on Data and Text Mining in Bioinformatics, DTMBIO 2008, Napa Valley, California, USA, October 30, 2008. ACM 2008, ISBN 978-1-60558-251-1 [contents] - 2007
- [c10]David W. Williams, Jun Huan, Wei Wang:
Graph Database Indexing Using Structured Graph Decomposition. ICDE 2007: 976-985 - [c9]Xiang Zhang, Wei Wang, Jun Huan:
On Demand Phenotype Ranking through Subspace Clustering. SDM 2007: 623-628 - [c8]Xueyi Wang, Jun Huan, Jack Snoeyink, Wei Wang:
Mining RNA Tertiary Motifs with Structure Graphs. SSDBM 2007: 31 - 2006
- [j2]Jun Huan, Jan F. Prins, Wei Wang:
Local Structure Comparison of Proteins. Adv. Comput. 68: 180-255 (2006) - [c7]Stephen Olivier, Jun Huan, Jinze Liu, Jan F. Prins, James Dinan, P. Sadayappan, Chau-Wen Tseng:
UTS: An Unbalanced Tree Search Benchmark. LCPC 2006: 235-250 - 2005
- [j1]Jun Huan, Deepak Bandyopadhyay, Wei Wang, Jack Snoeyink, Jan F. Prins, Alexander Tropsha:
Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs. J. Comput. Biol. 12(6): 657-671 (2005) - 2004
- [c6]Jun Huan, Wei Wang, Jan F. Prins, Jiong Yang:
SPIN: mining maximal frequent subgraphs from graph databases. KDD 2004: 581-586 - [c5]Jun Huan, Wei Wang, Anglina Washington, Jan F. Prins, Ruchir Shah, Alexander Tropsha:
Accurate Classification of Protein Structural Families Using Coherent Subgraph Analysis. Pacific Symposium on Biocomputing 2004: 411-422 - [c4]Jun Huan, Wei Wang, Deepak Bandyopadhyay, Jack Snoeyink, Jan F. Prins, Alexander Tropsha:
Mining protein family specific residue packing patterns from protein structure graphs. RECOMB 2004: 308-315 - 2003
- [c3]Jun Huan, Jan F. Prins, Wei Wang, Todd J. Vision:
Reconstruction of Ancestral Gene Order after Segmental Duplication and Gene Loss. CSB 2003: 484-485 - [c2]Jun Huan, Wei Wang, Jan F. Prins:
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism. ICDM 2003: 549-552 - [c1]Konstantin Berlin, Jun Huan, Mary Jacob, Garima Kochhar, Jan F. Prins, William W. Pugh, P. Sadayappan, Jaime Spacco, Chau-Wen Tseng:
Evaluating the Impact of Programming Language Features on the Performance of Parallel Applications on Cluster Architectures. LCPC 2003: 194-208
Coauthor Index
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