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ORCIDJuho Lee 0001
Person information
- affiliation: KAIST, Daejeon, South Korea
- affiliation: AITRICS
- affiliation (former): University of Oxford, UK
- affiliation (former): Pohang University of Science and Technology, South Korea
Other persons with the same name
- Juho Lee — disambiguation page
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2020 – today
- 2024
[c51]Dongjin Lee, Juho Lee, Kijung Shin:
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs. AAAI 2024: 13374-13382- [c50]Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, Juho Lee:
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion. ECCV (67) 2024: 128-145 - [c49]Moonseok Choi, Hyungi Lee, Giung Nam, Juho Lee:
Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning. ICLR 2024 - [c48]Hyunsu Kim, Jongmin Yoon, Juho Lee:
Fast Ensembling with Diffusion Schrödinger Bridge. ICLR 2024 - [c47]Dong Bok Lee, Seanie Lee, Joonho Ko, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Self-Supervised Dataset Distillation for Transfer Learning. ICLR 2024 - [c46]Hyungi Lee, Giung Nam, Edwin Fong, Juho Lee:
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling. ICLR 2024 - [c45]Giung Nam, Byeongho Heo, Juho Lee:
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance. ICLR 2024 - [c44]Seunghyun Kim, Seohyeon Jung, Seonghyeon Kim, Juho Lee:
Learning to Explore for Stochastic Gradient MCMC. ICML 2024 - [c43]Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee:
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts. ICML 2024 - [c42]Tae Hong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Jaewoong Cho, Juho Lee:
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models. ICML 2024 - [c41]Byung-Hoon Kim, Jungwon Choi, EungGu Yun, Kyungsang Kim, Xiang Li, Juho Lee:
Learning Dynamic Brain Connectome with Graph Transformers for Psychiatric Diagnosis Classification. ISBI 2024: 1-5 - [i62]Jongmin Yoon, Juho Lee:
Sequential Flow Straightening for Generative Modeling. CoRR abs/2402.06461 (2024) - [i61]Jungwon Choi, Hyungi Lee, Byung-Hoon Kim, Juho Lee:
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain. CoRR abs/2403.06432 (2024) - [i60]Hyungi Lee, Giung Nam, Edwin Fong, Juho Lee:
Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling. CoRR abs/2403.07282 (2024) - [i59]Giung Nam, Byeongho Heo, Juho Lee:
Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance. CoRR abs/2404.00860 (2024) - [i58]Hyunsu Kim, Jongmin Yoon, Juho Lee:
Fast Ensembling with Diffusion Schr\"odinger Bridge. CoRR abs/2404.15814 (2024) - [i57]Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park, Juho Lee, Sung Ju Hwang, Hae Beom Lee:
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation. CoRR abs/2405.17918 (2024) - [i56]Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Moksh Jain:
Learning diverse attacks on large language models for robust red-teaming and safety tuning. CoRR abs/2405.18540 (2024) - [i55]Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee:
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts. CoRR abs/2407.04271 (2024) - [i54]Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, Juho Lee:
Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion. CoRR abs/2407.21032 (2024) - [i53]Tae Hong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Jaewoong Cho, Juho Lee:
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models. CoRR abs/2408.05927 (2024) - [i52]Seunghyun Kim, Seohyeon Jung, Seonghyeon Kim, Juho Lee:
Learning to Explore for Stochastic Gradient MCMC. CoRR abs/2408.09140 (2024) - [i51]Seanie Lee, Haebin Seong, Dong Bok Lee, Minki Kang, Xiaoyin Chen, Dominik Wagner, Yoshua Bengio, Juho Lee, Sung Ju Hwang:
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models. CoRR abs/2410.01524 (2024) - 2023
- [j1]Hoil Lee, Fadhel Ayed, Paul Jung
, Juho Lee, Hongseok Yang, Francois Caron:
Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility. J. Mach. Learn. Res. 24: 289:1-289:78 (2023) - [c40]Sanghyun Kim, Jungwon Choi, NamHee Kim, Jaesung Ryu, Juho Lee:
Modeling Uplift from Observational Time-Series in Continual Scenarios. AAAI Bridge Program 2023: 75-84 - [c39]Seohyeon Jung, Sanghyun Kim, Juho Lee:
A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles. ICLR 2023 - [c38]Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi:
Self-Distillation for Further Pre-training of Transformers. ICLR 2023 - [c37]Youngwan Lee, Jeffrey Ryan Willette, Jonghee Kim, Juho Lee, Sung Ju Hwang:
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders. ICLR 2023 - [c36]Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee:
Martingale Posterior Neural Processes. ICLR 2023 - [c35]Giung Nam, Sunguk Jang, Juho Lee:
Decoupled Training for Long-Tailed Classification With Stochastic Representations. ICLR 2023 - [c34]Seunghyun Kim, Hyunsu Kim, Eunggu Yun, Hwangrae Lee, Jaehun Lee, Juho Lee:
Probabilistic Imputation for Time-series Classification with Missing Data. ICML 2023: 16654-16667 - [c33]Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee:
Regularizing Towards Soft Equivariance Under Mixed Symmetries. ICML 2023: 16712-16727 - [c32]Jeffrey Willette, Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation. ICML 2023: 37008-37041 - [c31]Eunggu Yun, Hyungi Lee, Giung Nam, Juho Lee:
Traversing Between Modes in Function Space for Fast Ensembling. ICML 2023: 40555-40577 - [c30]Balhae Kim, Hyungi Lee, Juho Lee:
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks. NeurIPS 2023 - [i50]Francois Caron, Fadhel Ayed, Paul Jung, Hoil Lee, Juho Lee, Hongseok Yang:
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning. CoRR abs/2302.01002 (2023) - [i49]Giung Nam, Sunguk Jang, Juho Lee:
Decoupled Training for Long-Tailed Classification With Stochastic Representations. CoRR abs/2304.09426 (2023) - [i48]Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee:
Martingale Posterior Neural Processes. CoRR abs/2304.09431 (2023) - [i47]Moonseok Choi, Hyungi Lee, Giung Nam, Juho Lee:
SWAMP: Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning. CoRR abs/2305.14852 (2023) - [i46]Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee:
Regularizing Towards Soft Equivariance Under Mixed Symmetries. CoRR abs/2306.00356 (2023) - [i45]Eunggu Yun, Hyungi Lee, Giung Nam, Juho Lee:
Traversing Between Modes in Function Space for Fast Ensembling. CoRR abs/2306.11304 (2023) - [i44]Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, Juho Lee:
Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models. CoRR abs/2307.05977 (2023) - [i43]Seunghyun Kim, Hyunsu Kim, Eunggu Yun, Hwangrae Lee, Jaehun Lee, Juho Lee:
Probabilistic Imputation for Time-series Classification with Missing Data. CoRR abs/2308.06738 (2023) - [i42]Dongjin Lee, Juho Lee, Kijung Shin:
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs. CoRR abs/2308.10779 (2023) - [i41]Dong Bok Lee, Seanie Lee, Joonho Ko, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Self-Supervised Dataset Distillation for Transfer Learning. CoRR abs/2310.06511 (2023) - [i40]Balhae Kim, Hyungi Lee, Juho Lee:
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks. CoRR abs/2310.17852 (2023) - [i39]Seongho Keum, Sanghyun Kim, Soojeong Lee, Juho Lee:
Slot-Mixup with Subsampling: A Simple Regularization for WSI Classification. CoRR abs/2311.17466 (2023) - [i38]Jungwon Choi, Seongho Keum, Eunggu Yun, Byung-Hoon Kim, Juho Lee:
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data. CoRR abs/2312.01994 (2023) - [i37]Byung-Hoon Kim, Jungwon Choi, Eunggu Yun, Kyungsang Kim, Xiang Li, Juho Lee:
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers. CoRR abs/2312.14939 (2023) - 2022
- [c29]Seanie Lee, Haebeom Lee, Juho Lee, Sung Ju Hwang:
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning. ICLR 2022 - [c28]Hyungi Lee, Eunggu Yun, Hongseok Yang, Juho Lee:
Scale Mixtures of Neural Network Gaussian Processes. ICLR 2022 - [c27]Jeffrey Ryan Willette, Hae Beom Lee, Juho Lee, Sung Ju Hwang:
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty. ICLR 2022 - [c26]Bruno Andreis, Seanie Lee, Tuan A. Nguyen, Juho Lee, Eunho Yang, Sung Ju Hwang:
Set Based Stochastic Subsampling. ICML 2022: 619-638 - [c25]Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee:
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation. ICML 2022: 16353-16367 - [c24]Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee:
On Divergence Measures for Bayesian Pseudocoresets. NeurIPS 2022 - [c23]Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Set-based Meta-Interpolation for Few-Task Meta-Learning. NeurIPS 2022 - [i36]Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, François Caron:
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility. CoRR abs/2205.08187 (2022) - [i35]Seanie Lee, Andreis Bruno, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang:
Set-based Meta-Interpolation for Few-Task Meta-Learning. CoRR abs/2205.09990 (2022) - [i34]Giung Nam, Hyungi Lee, Byeongho Heo, Juho Lee:
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation. CoRR abs/2206.15047 (2022) - [i33]Jeffrey Willette, Andreis Bruno, Juho Lee, Sung Ju Hwang:
Universal Mini-Batch Consistency for Set Encoding Functions. CoRR abs/2208.12401 (2022) - [i32]Youngwan Lee, Jeffrey Willette, Jonghee Kim, Juho Lee, Sung Ju Hwang:
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders. CoRR abs/2210.02077 (2022) - [i31]Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi:
Self-Distillation for Further Pre-training of Transformers. CoRR abs/2210.02871 (2022) - [i30]Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee:
On Divergence Measures for Bayesian Pseudocoresets. CoRR abs/2210.06205 (2022) - 2021
- [c22]Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang:
Learning to Perturb Word Embeddings for Out-of-distribution QA. ACL/IJCNLP (1) 2021: 5583-5595 - [c21]Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong:
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data. CVPR 2021: 15059-15068 - [c20]Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang:
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification. ICCV 2021: 8433-8442 - [c19]Jongmin Yoon, Sung Ju Hwang, Juho Lee:
Adversarial Purification with Score-based Generative Models. ICML 2021: 12062-12072 - [c18]Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee:
Diversity Matters When Learning From Ensembles. NeurIPS 2021: 8367-8377 - [c17]Andreis Bruno, Jeffrey Willette, Juho Lee, Sung Ju Hwang:
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding. NeurIPS 2021: 21365-21374 - [i29]Jeffrey Willette, Juho Lee, Sung Ju Hwang:
Improving Uncertainty Calibration via Prior Augmented Data. CoRR abs/2102.10803 (2021) - [i28]Andreis Bruno, Jeffrey Willette, Juho Lee, Sung Ju Hwang:
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding. CoRR abs/2103.01615 (2021) - [i27]Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong:
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data. CoRR abs/2103.15619 (2021) - [i26]Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang:
Learning to Perturb Word Embeddings for Out-of-distribution QA. CoRR abs/2105.02692 (2021) - [i25]Jongmin Yoon, Sung Ju Hwang, Juho Lee:
Adversarial purification with Score-based generative models. CoRR abs/2106.06041 (2021) - [i24]Saehoon Kim, Sungwoong Kim, Juho Lee:
Hybrid Generative-Contrastive Representation Learning. CoRR abs/2106.06162 (2021) - [i23]Jihoon Ko, Taehyung Kwon, Kijung Shin, Juho Lee:
Learning to Pool in Graph Neural Networks for Extrapolation. CoRR abs/2106.06210 (2021) - [i22]Hyungi Lee, Eunggu Yun, Hongseok Yang, Juho Lee:
Scale Mixtures of Neural Network Gaussian Processes. CoRR abs/2107.01408 (2021) - [i21]Seanie Lee, Haebeom Lee, Juho Lee, Sung Ju Hwang:
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning. CoRR abs/2110.02600 (2021) - [i20]Jeffrey Ryan Willette, Haebeom Lee, Juho Lee, Sung Ju Hwang:
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty. CoRR abs/2110.06381 (2021) - [i19]Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee:
Diversity Matters When Learning From Ensembles. CoRR abs/2110.14149 (2021) - 2020
- [c16]Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang:
Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare. AAAI 2020: 3649-3657 - [c15]Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang:
Cost-Effective Interactive Attention Learning with Neural Attention Processes. ICML 2020: 4228-4238 - [c14]Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi:
Neural Complexity Measures. NeurIPS 2020 - [c13]Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh:
Bootstrapping neural processes. NeurIPS 2020 - [i18]Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang:
Cost-effective Interactive Attention Learning with Neural Attention Processes. CoRR abs/2006.05419 (2020) - [i17]Tuan A. Nguyen, Andreis Bruno, Juho Lee, Eunho Yang, Sung Ju Hwang:
Stochastic Subset Selection. CoRR abs/2006.14222 (2020) - [i16]Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi:
Neural Complexity Measures. CoRR abs/2008.02953 (2020) - [i15]Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh:
Bootstrapping Neural Processes. CoRR abs/2008.02956 (2020) - [i14]Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski:
Attentive Clustering Processes. CoRR abs/2010.15727 (2020)
2010 – 2019
- 2019
- [c12]Juho Lee, Lancelot F. James, Seungjin Choi, Francois Caron:
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure. AISTATS 2019: 758-767 - [c11]Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang:
Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning. ICLR (Poster) 2019 - [c10]Fadhel Ayed, Juho Lee, Francois Caron:
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior. ICML 2019: 395-404 - [c9]Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh:
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. ICML 2019: 3744-3753 - [i13]Fadhel Ayed, Juho Lee, François Caron:
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior. CoRR abs/1902.04714 (2019) - [i12]Juho Lee, Xenia Miscouridou, François Caron:
A unified construction for series representations and finite approximations of completely random measures. CoRR abs/1905.10733 (2019) - [i11]Juho Lee, Yoonho Lee, Yee Whye Teh:
Deep Amortized Clustering. CoRR abs/1909.13433 (2019) - [i10]Tony Duan, Juho Lee:
Graph Embedding VAE: A Permutation Invariant Model of Graph Structure. CoRR abs/1910.08057 (2019) - 2018
- [c8]Jay Heo, Haebeom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang:
Uncertainty-Aware Attention for Reliable Interpretation and Prediction. NeurIPS 2018: 917-926 - [c7]Haebeom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang:
DropMax: Adaptive Variational Softmax. NeurIPS 2018: 927-937 - [i9]Jay Heo, Haebeom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang:
Uncertainty-Aware Attention for Reliable Interpretation and Prediction. CoRR abs/1805.09653 (2018) - [i8]Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Yi Yang:
Transductive Propagation Network for Few-shot Learning. CoRR abs/1805.10002 (2018) - [i7]Juho Lee, Saehoon Kim, Jaehong Yoon, Haebeom Lee, Eunho Yang, Sung Ju Hwang:
Adaptive Network Sparsification via Dependent Variational Beta-Bernoulli Dropout. CoRR abs/1805.10896 (2018) - [i6]Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang, Eunho Yang:
Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare. CoRR abs/1806.01551 (2018) - [i5]Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh:
Set Transformer. CoRR abs/1810.00825 (2018) - [i4]Juho Lee, Lancelot F. James, Seungjin Choi, François Caron:
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure. CoRR abs/1810.01778 (2018) - 2017
- [c6]Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi:
Bayesian inference on random simple graphs with power law degree distributions. ICML 2017: 2004-2013 - [i3]Haebeom Lee, Juho Lee, Eunho Yang, Sung Ju Hwang:
DropMax: Adaptive Stochastic Softmax. CoRR abs/1712.07834 (2017) - 2016
- [c5]Juho Lee, Lancelot F. James, Seungjin Choi:
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models. NIPS 2016: 3162-3170 - 2015
- [c4]Juho Lee, Seungjin Choi:
Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility. AISTATS 2015 - [c3]Juho Lee, Seungjin Choi:
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models. NIPS 2015: 1891-1899 - [i2]Juho Lee, Seungjin Choi:
Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility. CoRR abs/1501.07430 (2015) - [i1]Juho Lee, Seungjin Choi:
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models. CoRR abs/1511.05650 (2015) - 2014
- [c2]Juho Lee, Seungjin Choi:
Incremental Tree-Based Inference with Dependent Normalized Random Measures. AISTATS 2014: 558-566 - 2012
- [c1]Juho Lee, Suha Kwak, Bohyung Han, Seungjin Choi:
Online Video Segmentation by Bayesian Split-Merge Clustering. ECCV (4) 2012: 856-869
Coauthor Index
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