2024

• Chengkuan Hong, Christian R. Shelton, Jun Zhu. Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes, Journal of Machine Learning Research (JMLR), 25(384):1−67, 2024.

• Siwei Wang, Jun Zhu. Optimal Learning Policies for Differential Privacy in Multi-armed Bandits, Journal of Machine Learning Research (JMLR), 25(314):1−52, 2024.

• Liyuan Wang, Xingxing Zhang, Hang Su, Jun Zhu. A Comprehensive Survey of Continual Learning: Theory, Method and Application, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2024.

• Huanran Chen, Yinpeng Dong, Shitong Shao, Zhongkai Hao, Xiao Yang, Hang Su, Jun Zhu. Diffusion Models are Certifiably Robust Classifiers, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Yikai Wang, Xinzhou Wang, Zilong Chen, Zhengyi Wang, Fuchun Sun, Jun Zhu. Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu. PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Huayu Chen, Kaiwen Zheng, Hang Su, Jun Zhu. Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu. C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Huayu Chen, Guande He, Lifan Yuan, Ganqu Cui, Hang Su, Jun Zhu. Noise Contrastive Alignment of Language Models with Explicit Rewards, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Min Zhao, Hongzhou Zhu, Chendong Xiang, Kaiwen Zheng, Chongxuan Li, Jun Zhu. Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Yuezhou Hu, Jun Zhu, Jianfei Chen. S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li. On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Guande He, Kaiwen Zheng, Jianfei Chen, Fan Bao, Jun Zhu. Consistency Diffusion Bridge Models, Advances in Neural Information Processing Systems (NeurIPS), 2024.

• Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu. Robust Classification via a Single Diffusion Model, International Conference on Machine Learning (ICML), 2024.

• Shuyu Cheng, Yibo Miao, Yinpeng Dong, Xiao Yang, Xiao-Shan Gao, Jun Zhu. Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior, International Conference on Machine Learning (ICML), 2024.

• Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu. DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training, International Conference on Machine Learning (ICML), 2024.

• Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang, Minlie Huang. Towards Efficient Exact Optimization of Language Model Alignment, International Conference on Machine Learning (ICML), 2024.

• Yuezhou Hu, Kang Zhao, Weiyu Huang, Jianfei Chen, Jun Zhu. Accelerating Transformer Pre-training with 2:4 Sparsity, International Conference on Machine Learning (ICML), 2024.

• Haocheng Xi, Yuxiang Chen, Kang Zhao, KAI JUN TEH, Jianfei Chen, Jun Zhu. Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization, International Conference on Machine Learning (ICML), 2024.

• Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying, Xingxing Zhang, Hang Su, Jun Zhu. Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning, International Conference on Machine Learning (ICML), 2024.

• Huanran Chen, Yichi Zhang, Yinpeng Dong, Xiao Yang, Hang Su, Jun Zhu. Rethinking Model Ensemble in Transfer-based Adversarial Attacks, International Conference on Learning Representations (ICLR), Vienna Austria, 2024.

• Lingxuan Wu, Xiao Yang, Yinpeng Dong, Liuwei XIE, Hang Su, Jun Zhu. Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches, International Conference on Learning Representations (ICLR), Vienna Austria, 2024.

• Jianhui Li, Shilong Liu, Zidong Liu, Yikai Wang, Kaiwen Zheng, Jinghui Xu, Jianmin Li, Jun Zhu. InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image, International Conference on Learning Representations (ICLR), Vienna Austria, 2024.

• Ziteng Wang, Jianfei Chen, Jun Zhu. Efficient Backpropagation with Variance Controlled Adaptive Sampling, International Conference on Learning Representations (ICLR), Vienna Austria, 2024.

• Huayu Chen, Cheng Lu, Zhengyi Wang, Hang Su, Jun Zhu. Score Regularized Policy Optimization through Diffusion Behavior, International Conference on Learning Representations (ICLR), Vienna Austria, 2024.

2023

• Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong. Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence, Nature Machine Intelligence, 5, pages:1356–1368, 2023. (Cover Paper)

• Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu. ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023. (Spotlight, Accept rate~3.6%)

• Bingrui Li, Jianfei Chen, Jun Zhu. Memory Efficient Optimizers with 4-bit States, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023. (Spotlight, Accept rate~3.6%)

• Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, Jun Zhu. Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023. (Spotlight, Accept rate~3.6%)

• Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu. DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023.

• Zhijie Deng, Peng Cui, Jun Zhu. Towards Accelerated Model Training via Bayesian Data Selection, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023.

• Haocheng Xi, ChangHao Li, Jianfei Chen, Jun Zhu. Training Transformers with 4-bit Integers, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023.

• Peng Cui, Dan Zhang, Zhijie Deng, Yinpeng Dong, Jun Zhu. Learning Sample Difficulty from Pre-trained Models for Reliable Prediction, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023.

• Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu. Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023.

• Yilin Lyu, Liyuan Wang, Xingxing Zhang, Zicheng Sun, Hang Su, Jun Zhu, Liping Jing. Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation, In Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023.

• Fan Bao, Shen Nie, Kaiwen Xue, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu. One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu. Stabilizing GANs’ Training with Brownian Motion Controller, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu. Exact Energy-Guided Diffusion Sampling via Contrastive Energy Prediction, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu. Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu. Revisiting Discriminative vs. Generative Classifiers: Theory and Implications, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu. NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu. MultiAdam: Parameter-wise Scale-invariant Optimizer for Physics-informed Neural Network, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Zhongkai Hao, Chengyang Ying, Zhengyi Wang, Hang Su, Yinpeng Dong, Songming Liu, Ze Cheng, Jun Zhu, Jian Song. GNOT: A General Neural Operator Transformer for Operator Learning, In proc. of International Conference on Machine Learning (ICML), Hawaii, USA, 2023.

• Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, yunfeng shao, Qibin Wu, Jun Zhu. A Constrained Bayesian Approach to Out-of-Distribution Prediction, In proc. of Uncertainty in Artificial Intelligence (UAI), Pittsburg, PA, USA, 2023.

• Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu. On the Reuse Bias in Off-Policy Reinforcement Learning, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2023.

• Guande He, Jianfei Chen, Jun Zhu. Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

• Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu. Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

• Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu. Equivariant Energy-Guided SDE for Inverse Molecular Design, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

• Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel Ni, Heung-Yeung Shum. DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

• Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng. Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients, In proc. of International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023.

• Nanyang Ye, Lin Zhu, Jia Wang, Zhaoyu Zeng, Jiayao Shao, Chensheng Peng, Bikang Pan, Kaican Li, Jun Zhu. Certifiable Out-of-Distribution Generalization, In proc. of AAAI Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023.

• Shilong Liu, Shijia Huang, Feng Li, Hao Zhang, Yaoyuan Liang, Hang Su, Jun Zhu, Lei Zhang. DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding, In proc. of AAAI Conference on Artificial Intelligence (AAAI), Washington DC, USA, 2023.

• Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu. All are Worth Words: A ViT Backbone for Diffusion Models, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023.

• Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu. PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023.

• Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu. Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023.

• Yinpeng Dong, Caixin Kang, Jinlai Zhang, Zijian Zhu, Yikai Wang, Xiao Yang, Hang Su, Xingxing Wei, Jun Zhu. Benchmarking Robustness of 3D Object Detection to Common Corruptions, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver Canada, 2023.

2022

• Ziyu Wang, Yuhao Zhou, Jun Zhu. Fast Instrument Learning with Faster Rates, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Zhijie Deng, Feng Zhou, Jun Zhu. Accelerated Linearized Laplace Approximation for Bayesian Deep Learning, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu. DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022. (Oral, Accept rate~1.7%)

• Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu. Censored Quantile Regression Neural Networks, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022. (Oral, Accept rate~1.7%)

• Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu. EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Peng Cui, Yang Yue, Zhijie Deng, Jun Zhu. Confidence-based Reliable Learning under Dual Noises, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu. On Viewpoint Robustness of Visual Recognition in the Wild, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Yibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao. Isometric 3D Adversarial Examples in the Physical World, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng. A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs, In proc. of Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.

• Qi-An Fu, Yinpeng Dong, Hang Su, Jun Zhu, Chao Zhang. AutoDA: Automated Decision-based Iterative Adversarial Attacks, In proc. of 31st USENIX Security Symposium (USENIX Security '22 Winter), Boston, MA, USA, 2022.

• Siwei Wang, Jun Zhu. Thompson Sampling for (Combinatorial) Pure Exploration, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Zhijie Deng, Jiaxin Shi, Jun Zhu. NeuralEF: Deconstructing Kernels by Deep Neural Networks, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang. Fast Lossless Neural Compression with Integer-Only Discrete Flows, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang. Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen, Jun Zhu. Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu. GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan. Robustness and Accuracy Could Be Reconcilable by (Proper) Definition, In proc. of International Conference on Machine Learning (ICML), Baltimore, Maryland USA, 2022.

• Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang. Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2022. (Oral, Accept rate~1.6%, Outstanding Paper Award)

• Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu. Exploring Memorization in Adversarial Training, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2022.

• Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang. DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2022.

• Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing HONG, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu. Memory Replay with Data Compression for Continual Learning, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2022.

• Tim Pearce, Jun Zhu. Counter-Strike Deathmatch with Large-Scale Behavioural Cloning, In proc. of IEEE Conference on Games (CoG), Beijing, China, 2022. (Best Paper Award)

• Shiyu Huang, Chao Yu, Bin Wang, Dong Li, Yu Wang, Ting Chen, Jun Zhu. VMAPD: Generate Diverse Solutions for Multi-Agent Games with Recurrent Trajectory Discriminators, In proc. of IEEE Conference on Games (CoG), Beijing, China, 2022. (Best Paper Nomination)

• Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu. Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Online (due to COVID-19), 2022. (Long Oral, Accept rate~3.8%)

• ChengYang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu. Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk, In proc. of International Joint Conference on Artificial Intelligence (IJCAI), Online (due to COVID-19), 2022.

• Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu. Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart, In proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2022.

• Hongyang Gu, Jianmin Li, Guangyuan Fu, Chifong Wong, Xinghao Chen, Jun Zhu. AutoLoss-GMS: Searching Generalized Margin-based Softmax Loss Function for Person Re-identification, In proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2022.

• Nanyang Ye, Kaican Li, Haoyue Bai, Runpeng Yu, Lanqing Hong, Fengwei Zhou, Zhenguo Li, Jun Zhu. OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization, In proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2022. (Oral)

• Jialian Li, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu. Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model, In proc. of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Online (due to COVID-19), 2022.

• Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Hang Su, Jun Zhu. Tianshou: A Highly Modularized Deep ReinforcementLearning Library, Journal of Machine Learning Research, in press, 2022. (Github with 5k+ stars: TianShou)

• Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu. Efficient Inference for Dynamic Flexible Interactions of Neural Populations, Journal of Machine Learning Research, in press, 2022.

• Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang. Triple Generative Adversarial Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), in press, 2022.

• Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, Jun Zhu. Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), in press, 2022.

• Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi. Brain-inspired global-local learning incorporated with neuromorphic computing, Nature Communications 13, Article number: 65, 2022.

2021

• Shuyu Cheng, Guoqiang Wu, Jun Zhu. On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

• Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu. Scalable Quasi-Bayesian Inference for Instrumental Variable Regression, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

• Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu. Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

• Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang. Stability and Generalization of Bilevel Programming in Hyperparameter Optimization, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

• Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu. Accumulative Poisoning Attacks on Real-time Data, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

• Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong. AFEC: Active Forgetting of Negative Transfer in Continual Learning, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2021.

• Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang. Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models, In proc. of International Conference on Machine Learning (ICML), Online (due to COVID-19), 2021.

• Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu. Implicit Normalizing Flows, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2021. (Spotlight, Accept rate~5.5%)

• Tsung Wei Tsai, Chongxuan Li, Jun Zhu. MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2021.

• Feng Zhou, Yixuan Zhang, Jun Zhu. Efficient Inference of Flexible Interaction in Spiking-neuron Networks, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2021.

• Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu. Bag of Tricks for Adversarial Training, In proc. of International Conference on Learning Representations (ICLR), Online (due to COVID-19), 2021.

• Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu. Black-box Detection of Backdoor Attacks with Limited Information and Data, In proc. of International Conference on Computer Vision (ICCV), Online (due to COVID-19), 2021.

• Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue. Towards Face Encryption by Generating Adversarial Identity Masks, In proc. of International Conference on Computer Vision (ICCV), Online (due to COVID-19), 2021.

• Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu. LiBRe: A Practical Bayesian Approach to Adversarial Detection, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2021.

• Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu. Unsupervised Part Segmentation through Disentangling Appearance and Shape, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2021.

• Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu. ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2021.

• Zihao Xiao, Xianfeng Gao, Chilin Fu, Yinpeng Dong, Wei Gao, Xiaolu Zhang, Jun Zhou, Jun Zhu. Improving Transferability of Adversarial Patches on Face Recognition with Generative Models, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online (due to COVID-19), 2021.

• Jialian Li, Chao Du, Jun Zhu. A Bayesian Approach for Subset Selection in Contextual Bandits, In proc. of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Online (due to COVID-19), 2021.

• Yong Ren, Yucen Luo, Jun Zhu. Improving Generative Moment Matching Networks with Distribution Partition, In proc. of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Online (due to COVID-19), 2021.

• Haosheng Zou, Tongzheng Ren, Dong Yan, Hang Su, Jun Zhu. Learning Task-Distribution Reward Shaping with Meta-Learning, In proc. of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Online (due to COVID-19), 2021.

• Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf. Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings, To Appear in proc. of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), Online (due to COVID-19), 2021.

• Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong. Triple Memory Networks: a Brain-Inspired Method for Continual Learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press.

2020

• Guoqiang Wu, Jun Zhu. Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Peng Cui, Wenbo Hu, Jun Zhu. Calibrated Reliable Regression using Maximum Mean Discrepancy, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu. Understanding and Exploring the Network with Stochastic Architectures, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Ziyu Wang, Bin Dai, David Wipf, Jun Zhu. Further Analysis of Outlier Detection with Deep Generative Models, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu. Efficient Learning of Generative Models via Finite-Difference Score Matching, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su. Adversarial Distributional Training for Robust Deep Learning, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu, Bo Zhang. Bi-level Score Matching for Learning Energy-based Latent Variable Models, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su. Boosting Adversarial Training with Hypersphere Embedding, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Online (due to COVID-19), 2020.

• Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian. VFlow: More Expressive Generative Flows with Variational Data Augmentation, In proc. of International Conference on Machine Learning (ICML), Vienna, Austria, 2020.

• Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang. Understanding and Stabilizing GANs' Training Dynamics Using Control Theory, In proc. of International Conference on Machine Learning (ICML), Vienna, Austria, 2020.

• Yuhao Zhou, Jiaxin Shi, Jun Zhu. Nonparametric Score Estimators, In proc. of International Conference on Machine Learning (ICML), Vienna, Austria, 2020.

• Michael Zhu, Chang Liu, Jun Zhu. Variance Reduction and Quasi-Newton for Particle-Based Variational Inference, In proc. of International Conference on Machine Learning (ICML), Vienna, Austria, 2020.

• Kun Xu, Chao Du, Chongxuan Li, Jun Zhu, Bo Zhang. Learning Implicit Generative Models By Teaching Density Estimators, In proc. of European Conference on Machine Learning (ECML), Ghent, Belgium, 2020.

• Jialian Li, Yichi Zhou, Tongzheng Ren, Jun Zhu. Exploration Analysis in Finite-Horizon Turn-based Stochastic Games, In proc. of Conference on Uncertainty in Artificial Intelligence (UAI), Toronto, Canada, 2020.

• Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang. A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models, In proc. of International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy, 2020.

• Yueru Li, Shuyu Cheng, Hang Su, Jun Zhu. Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds, In proc. of European Conference on Computer Vision (ECCV), Online (COVID-19 pandemic), 2020.

• Xiao Yang, Fangyun Wei, Hongyang Zhang, Jun Zhu. Design and Interpretation of Universal Adversarial Patches in Face Detection, In proc. of European Conference on Computer Vision (ECCV), Online (COVID-19 pandemic), 2020.

• Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang. Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters, In proc. of European Conference on Computer Vision (ECCV), Online (COVID-19 pandemic), 2020. (Oral, Accept rate~2%)

• Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu. Benchmarking Adversarial Robustness on Image Classification, In proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, 2020. (Oral, Accept rate~5%)

• Yichi Zhou, Jialian Li, and Jun Zhu. Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information, In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. (Oral, Accept rate<1.9%)

• Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, and Ricky T. Q. Chen. SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models, In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. (Spotlight, Accept rate<6%)

• Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, and Bo Zhang. To Relieve Your Headache of Training an MRF, Take AdVIL, In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

• Yichi Zhou, Tongzheng Ren, Jialian Li, Dong Yan, and Jun Zhu. Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information , In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

• Tianyu Pang, Kun Xu, and Jun Zhu. Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks, In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

• Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, and Jun Zhu. Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness, In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

• Shiyu Huang, Hang Su, Jun Zhu, and Ting Chen. SVQN: Sequential Variational Soft Q-Learning Networks, In proc. of International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

• Jian Wu , Changran Hu , Yulong Wang , Xiaolin Hu , and Jun Zhu. A Hierarchical Recurrent Neural Network for Symbolic Melody Generation, IEEE Transactions on Cybernetics, in press, 2020.

• Jian Wu, Xiaoguang Liu, Xiaolin Hu, Jun Zhu. PopMNet: Generating structured pop music melodies using neural networks, Artificial Intelligence, in press, 2020.

2019

• Justin Cosentino, and Jun Zhu. Generative Well-intentioned Networks, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.

• Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Improving Black-box Adversarial Attacks with a Transfer-based Prior, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.

• Kun Xu, Chongxuan Li, Jun Zhu, and Bo Zhang. Multi-objects Generation with Amortized Structural Regularization, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.

• Chang Liu, Jingwei Zhuo, and Jun Zhu. Understanding MCMC Dynamics as Flows on the Wasserstein Space, In Proc. of International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA, 2019.

• Chang Liu, Jingwei Zhuo, Pengyu Chen, Ruiyi Zhang, and Jun Zhu. Understand and Accelerate Particle-based Variational Inference, In Proc. of International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA, 2019.

• Jiaxin Shi, Mohammad Khan, and Jun Zhu. Scalable Training of Inference Networks for Gaussian-Process Models, In Proc. of International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA, 2019.

• Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. Improving Adversarial Robustness via Promoting Ensemble Diversity, In Proc. of International Conference on Machine Learning (ICML 2019), Long Beach, CA, USA, 2019.

• Ziyu Wang, Tongzheng Ren, Jun Zhu, and Bo Zhang. Function Space Particle Optimization for Bayesian Neural Networks, In Proc. of International Conference on Learning Representations (ICLR 2019), New Orleans, Louisiana, USA, 2019.

• Zhijie Deng, Yucen Luo, and Jun Zhu. Cluster Alignment With a Teacher for Unsupervised Domain Adaptation, In Proc. of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, 2019.

• Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, 2019. (Oral)

• Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu. Efficient Decision-based Black-box Adversarial Attacks on Face Recognition, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, 2019.

• Shihong Song, Jiayi Weng, Hang Su, Dong Yan, Haosheng Zou, and Jun Zhu. Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning, In Proc. of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macau, China, 2019.

• Jialian Li, Tongzheng Ren, Hang Su, and Jun Zhu. Learn a Robust Policy in Adversarial Games via Playing with an Expert Opponent, In Proc. of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS-19), Montreal, Canada, 2019.

• Xingxing Wei, Jun Zhu, Hang Su and Sha Yuan. Sparse Adversarial Perturbations for Videos, To appear in the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, USA, 2019.

• Shiyu Huang, Hang Su, Jun Zhu, and Tim Chen. Combo-Action: Training Agent For FPS Game with Auxiliary Tasks, To appear in the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, USA, 2019.

• Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. Direct Training for Spiking Neural Networks: Faster, Larger, Better, To appear in the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, USA, 2019.

• You Qiaoben, Zheng Wang, Jianguo Li, Yu-Gang Jiang, Jun Zhu and Yinpeng Dong. Composite Binary Decomposition Network, To appear in the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, Hawaii, USA, 2019.

• Julia Spilcke-Liss, Jun Zhu, Sebastian Gluth, Michael L. Spezio, and Jan Glascher. Semantic Incongruency Interferes with Endogenous Attention in Cross-Modal Integration of Semantically Congruent Objects , Frontiers in Integrative Neuroscience, in press, 2019.

• Nikolai Zakharov, Hang Su, Jun Zhu, and Jan Glascher. Towards Controllable Image Descriptions with Semi-Supervised VAE, Journal of Visual Communication and Image Representation (JVCI), in press, 2019.

• Zhize Li, Tianyi Zhang, Shuyu Cheng, Jun Zhu, and Jian Li. Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference, Machine Learning, 108(8-9):1701-1727, 2019.

• Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Hang Su, and Jun Zhu. Stochastic Quantization for Learning Accurate Low-bit DeepNeural Networks, International Journal of Computer Vision (IJCV), in press, 2019.

2018

• Tian Tian, Jun Zhu, and You Qiaoben. Max-Margin Majority Voting for Learning from Crowds, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), in press, 2018.

• Jianfei Chen, Jun Zhu, Yee Whye Teh and Tong Zhang. Stochastic Expectation Maximization with Variance Reduction, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018.

• Tianyu Pang, Chao Du, Yinpeng Dong and Jun Zhu. Towards Robust Detection of Adversarial Samples, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018. (Spotlight, NVIDIA Pioneering Research Award) (preprint: arXiv:1706.00633) (NVIDIA Pioneering Research Award)

• Chongxuan Li, Max Welling, Jun Zhu and Bo Zhang. Graphical Generative Adversarial Networks, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018. (preprint: arXiv:1804.03429)

• Yucen Luo, Tian Tian, Jiaxin Shi, Jun Zhu and Bo Zhang. Semi-crowdsourced Clustering with Deep Generative Models, In proc. of Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018.

• Jianfei Chen, Jun Zhu, and Le Song. Stochastic Training of Graph Convolutional Networks, In Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.

• Yichi Zhou, Jun Zhu, and Jingwei Zhuo. Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors, In Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.

• Tianyu Pang, Chao Du, and Jun Zhu. Max-Mahalanobis Linear Discriminant Analysis Networks, In Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.

• Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. Adversarial Attack on Graph Structured Data, In Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.

• Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang. Message Passing Stein Variational Gradient Descent, In Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.

• Jiaxin Shi, Shengyang Sun, and Jun Zhu. A Spectral Approach to Gradient Estimation for Implicit Distributions, In Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.

• Juzheng Li, Hang Su, Jun Zhu, and Bo Zhang. Essay-Anchor Attentive Multi-Modal Bilinear Pooling for Textbook Question Answering, In Proc. of IEEE International Conference on Multimedia and Expo (ICME), San Diego, USA, 2018. (Platinum Best Paper Award)

• Xingxing Wei, Jun Zhu, Sitong Feng, and Hang Su. Video-to-Video Translation with Global Temporal Consistency, In Proc. of ACM Multimedia (MM), Seoul, Korea, 2018.

• Jiaxin Shi, Shengyang Sun, and Jun Zhu. Kernel Implicit Variational Inference, In Proc. of the 6th International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, 2018.

• Danyang Sun, Tongzheng, Ren, Chongxuan Li, Hang Su, and Jun Zhu. Learning to Write Stylized Chinese Characters by Reading a Handful of Examples, In Proc. of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 2018.

• Jianfei Chen, Jun Zhu, Jie Lu and Shixia Liu . Scalable Training of Hierarchical Topic Models, In Proc. of the 44th International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, 2018.

• Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, and Bo Zhang. Smooth Neighbors on Teacher Graphs for Semi-supervised Learning, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018.

• Juzheng Li, Hang Su, Jun Zhu, Siyu Wang, and Bo Zhang. Textbook Question Answering under Instructor Guidance with Memory Networks, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018.

• Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting Adversarial Attacks with Momentum, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. (We won the first places in both " Non-targeted Adversarial Attack" and " Targeted Adversarial Attacks" of NIPS 2017 Adversarial Attacks and Defenses)

• Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, and Xiaolin Hu. Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018. (We won the first place in " Defense Against Adversarial Attack" of NIPS 2017 Adversarial Attacks and Defenses)

• Chang Liu and Jun Zhu. Riemannian Stein Variational Gradient Descent for Bayesian Inference, In Proc. of 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.

• Zihao Xiao, Jianfei Chen, and Jun Zhu. Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems, In Proc. of 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.

• Tian Tian, Yichi Zhou, and Jun Zhu. Selective Verification Strategy for Learning from Crowds, In Proc. of 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.

• Haosheng Zou, Hang Su, Shihong Song, and Jun Zhu. Understanding Human Behavior in Crowds by Imitating the Decision-Making Process, In Proc. of 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.

• Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, and Bo Zhang. Collaborative Filtering with User-Item Co-Autoregressive Models, In Proc. of 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.

• Mengchen Liu, Shixia Liu, Hang Su, Kelei Cao, and Jun Zhu. Analyzing the Noise Robustness of Deep Neural Networks, IEEE Conference on Visual Analytics Science and Technology (VAST), Berlin, Germany, 2018

• Mowei Wang, Yong Cui, Shihan Xiao, Xin Wang, Dan Yang, Kai Chen, and Jun Zhu. Neural Network Meets DCN: Traffic-driven Topology Adaption with Deep Learning, In Proc. of SIGMETRICS, Irvine, California, USA, 2018.

• Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks, Frontiers in Neuroscience-Neuromorphic Engineering (in press), 2018.

• Ning Chen, Jun Zhu, Jianfei Chen, and Ting Chen. Dropout Training for SVMs with Data Augmentation, Frontiers of Computer Science, 12(4):694--713, 2018. (arXiv version)

2017

• Chongxuan Li, Jun Zhu, and Bo Zhang. Max-Margin Deep Generative Models for (Semi-)Supervised Learning, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 40(11):2762--2775, 2018.

• Yong Ren, Yining Wang, and Jun Zhu. Spectral Learning for Supervised Topic Models, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 40(3):726--739, 2018.

• Tianlin Shi, and Jun Zhu. Online Bayesian Passive Aggressive Learning, Journal of Machine Learning Research (JMLR), 18(33):1-39, 2017 (a preliminary version was published at ICML 2014, Scoring top 18 in 1260+ submissions) [Code]

• Jun Zhu, Jianfei Chen, Wenbo Hu, and Bo Zhang. Big Learning with Bayesian Methods, National Science Review (NSR), nwx044. doi: 10.1093/nsr/nwx044, (arXiv:1411.6370), 2017

• Chao Du, Jun Zhu, and Bo Zhang. Learning Deep Generative Models with Doubly Stochastic MCMC, IEEE Trans. on Neural Networks and Learning Systems (TNNLS), in press, 2017. (preprint at arXiv:1506.04557, 2015.)

• Chongxuan Li, Kun Xu, Jun Zhu, and Bo Zhang. Triple Generative Adversarial Nets, In Proc. of Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, 2017 (arXiv preprint)

• Jianfei Chen, Chongxuan Li, Yizhong Ru, and Jun Zhu. Population Matching Discrepancy and Applications in Deep Learning, In Proc. of Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, 2017

• Zhijie Deng, Hao Zhang, Xiaodan Liang, Jun Zhu, and Eric Xing. Structured Generative Adversarial Networks, In Proc. of Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, 2017 (NVIDIA Pioneering Research Award)

• Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, and Bo Zhang. Smooth Neighbors on Teacher Graphs for Semi-supervised Learning, In Proc. of Advances in Neural Information Processing Systems (NIPS) Workshop on Learning with Limited Labeled Data, Long Beach, CA, 2017 (Best Paper Award)

• Yichi Zhou, Jialian Li, and Jun Zhu. Identify the Nash Equilibrium in Static Games with Random Payoffs, In Proc. of International Conference on Machine Learning (ICML), Sydney, Australia, 2017 (NVIDIA Pioneering Research Award)

• Jiaxin Shi, Shengyang Sun, and Jun Zhu. Implicit Variational Inference with Kernel Density Ratio Fitting, In Proc. of International Conference on Machine Learning (ICML) Workshop on Implicit Models, Sydney, Australia, 2017

• Yinpeng Dong, Renkun Ni, Jianguo Li, Yurong Chen, Jun Zhu, and Hang Su. Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization, To Appear in the 28th British Machine Vision Conference (BMVC), London, 2017 (Oral, Best Paper Nomination)

• Yong Ren, and Jun Zhu. Distributed Accelerated Proximal Coordinate Gradient Methods, In Proc. of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017

• Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, and Bo Zhang. Semi-supervised Max-margin Topic Models with Manifold Posterior Regularization, In Proc. of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017

• Hang Su, Jun Zhu, Yinpeng Dong, and Bo Zhang. Forecast the Plausible Paths in Crowd Scenes, In Proc. of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017

• Liu Jiang, Mengchen Liu, Junlin Liu, Xiting Wang, Jun Zhu, and Shixia Liu. Improving Learning-from-Crowds through Expert Validation, In Proc. of International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017

• Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, and Shixia Liu. Analyzing the Training Processes of Deep Generative Models, IEEE Transactions on Visualization and Computer Graphics (accepted). 24(1), 2017

• Shixia Liu, Jiannan Xiao, Junlin Liu, Xiting Wang, Jing Wu, and Jun Zhu. Visual Diagnosis of Tree Boosting Methods, IEEE Transactions on Visualization and Computer Graphics (accepted). 24(1), 2017

• Yinpeng Dong, Hang Su, Jun Zhu, and Bo Zhang. Improving Interpretability of Deep Neural Networks with Semantic Information, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2017

• Tian Tian, Ning Chen, and Jun Zhu. Learning Attributes from the Crowdsourced Relative Labels, In Proc. of Thirty-First AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California, 2017

• Kaiwei Li, Jianfei Chen, Wenguang Chen, and Jun Zhu. SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs, In Proc. of Architectural Support for Programming Languages and Operating Systems (ASPLOS), Xi'an, China, 2017 (arXiv preprint: arXiv:1610.02496v2)

• Jun Zhu and Bei Chen. Latent feature models for large-scale link prediction, Big Data Analytics, in press, 2017

2016

• Chang Liu, Jun Zhu, and Yang Song. Stochastic Gradient Geodesic MCMC Methods, In Proc. of Advances in Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016 (NIPS 2016).

• Yang Song, Jun Zhu, and Yong Ren. Kernel Bayesian Inference with Posterior Regularization, In Proc. of Advances in Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016 (NIPS 2016).

• Yong Ren, Jialian Li, Yucen Luo, and Jun Zhu. Conditional Generative Moment-Matching Networks, In Proc. of Advances in Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016 (NIPS 2016).

• Chongxuan Li, Jun Zhu, and Bo Zhang. Learning to Generate with Memory, In Proc. of International Conference on Machine Learning (ICML), New York, USA, 2016 (ICML 2016) [Code] (preprint: arXiv:1602.07416).

• Pengtao Xie, Jun Zhu, and Eric P. Xing. Diversity-Promoting Bayesian Learning of Latent Variable Models, In Proc. of International Conference on Machine Learning (ICML), New York, USA, 2016 (ICML 2016).

• Jianfei Chen, Kaiwei Li, Jun Zhu, and Wenguang Chen. WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation, In Proc. of International Conference on Very Large Data Bases (VLDB), New Delhi, India, 2016 (VLDB 2016) [Code] (preprint at arXiv:1510.08628v2).

• Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu. Towards Better Analysis of Deep Convolutional Neural Networks, IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2016, TVCG track, 23(1): 91-100), Maryland, USA [Demo](preprint at: arXiv:1604.07043), 2016. (Top-2 most popular article at TVCG)

• Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu. Interactive Demo: A Visual Analysis System for Analyzing Deep Convolutional Neural Networks, International Conference on Machine Learning (ICML) Workshop on Visualization for Deep Learning, New York, USA, 2016 (ICML 2016, Workshop).

• Hang Su, Jun Zhu, Zhaozheng Yin, Yinpeng Dong, and Bo Zhang. Efficient and Robust Semi-supervised Learning over a Sparse-Regularized Graph, In Proc. of European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, 2016 (ECCV 2016).

• Juzheng Li, Jun Zhu, and Bo Zhang. Discriminative Deep Random Walk for Network Classification, In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, Germany, 2016 (ACL 2016).

• Jingwei Zhuo, Yong Cao, Jun Zhu, Bo Zhang, and Zaiqing Nie. Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields, In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, Germany, 2016 (ACL 2016).

• Shaohua Li, Tat-Seng Chua, Jun Zhu, and Chunyan Miao. Generative Topic Embedding: a Continuous Representation of Documents, In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, Germany, 2016 (ACL 2016) [Code].

• Shaohua Li, Jun Zhu, and Chunyan Miao. PSDVec: a Toolbox for Incremental and Scalable Word Embedding , Neurocomputing, 2016. [Code].

• Yuan Yang, Jianfei Chen, and Jun Zhu. Distributing the Stochastic Gradient Sampler for Large-Scale LDA, In Proc. of SIGKDD Conference on on Knowledge Discovery and Data Mining (KDD), San Francisco, 2016 (SIGKDD 2016).

• Hang Su, Yinpeng Dong, Jun Zhu, Haibin Ling, and Bo Zhang. Crowd Scene Understanding with Coherent Recurrent Neural Networks, In Proc. of International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 2016 (IJCAI 2016).

• Xiting Wang, Shixia Liu, Junlin Liu, Jianfei Chen, Jun Zhu, and Baining Guo. TopicPanorama: A Full Picture of Relevant Topics, IEEE Transactions on Visualization and Computer Graphics, (TVCG 2016. IEEE TVCG spotlight article for Dec. 2016)

• Wenbo Hu, Jun Zhu, and Bo Zhang. Fast sampling methods for Bayesian max-margin models, Knowledge-Based Systems, 97:277-287, 2016.

• Wenhao Zhang, Jianqiu Ji, Jun Zhu, Jianmin Li, and Bo Zhang. BitHash: An efficient bitwise Locality Sensitive Hashing method with applications, Knowledge-Based Systems, 97:40-47, 2016.

• Arnab Bhadury, Jianfei Chen, Jun Zhu, and Shixia Liu. Scaling up Dynamic Topic Models, In Proc. of World Wide Web Conference (WWW), Montreal, Canada, 2016. (WWW 2016)

• Yang Song, and Jun Zhu. Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization, In Proc. of AAAI Conference on Artificial Intelligence (AAAI), Phoenix, USA, 2016. (AAAI 2016)

• Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, and Bo Zhang. Jointly Modeling Topics and Intents with Global Order Structure, In Proc. of AAAI Conference on Artificial Intelligence (AAAI), Phoenix, USA, 2016. (AAAI 2016)

• Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, and Bo Zhang. Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation, In Proc. of AAAI Conference on Artificial Intelligence (AAAI), Phoenix, USA, 2016. (AAAI 2016)

2015

• Chongxuan Li, Jun Zhu, Tianlin Shi, and Bo Zhang. Max-margin Deep Generative Models, In Proc. of Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, 2015. (NIPS 2015) [Code] [code]

• Tian Tian, and Jun Zhu. Max-margin Majority Voting for Learning from Crowds, In Proc. of Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, 2015. (NIPS 2015)

• Jiaxin Shi, and Jun Zhu. Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base, In NIPS 2015 Cognitive Computation workshop, Montreal, Canada, 2015. CoCo@NIPS 2015)

• Yining Wang, and Jun Zhu. DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics, In Proc. of International Conference on Machine Learning (ICML), Lille, France, 2015. (ICML 2015)

• Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh, and Jun Zhu. Interactive Cell Segmentation based on Active and Semi-supervised Learning, IEEE Transactions on Medical Image Analysis, in press, 2015.

• Jingwei Zhuo, Jun Zhu, and Bo Zhang. Adaptive Dropout Rates for Learning with Corrupted Features, In Proc. of International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, 2015. (IJCAI 2015, full oral)

• Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, and Tong Zhang.Crowd Fraud Detection in Internet Advertising, In Proc. of International World Wide Web Conference (WWW), Florence, Italy, 2015. (WWW 2015, full oral)

• Shaohua Li, Jun Zhu, and Chunyan Miao.A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution, In Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP), Lisbon, Portugal, 2015. (EMNLP 2015)

• Qi Lv, Zhiyong Wu, and Jun Zhu.Polyphonic Music Modelling with LSTM-RTRBM, In Proc. of ACM Multimedia, Brisbane, Australia, 2015. (MM 2015)

• Tian Tian, and Jun Zhu. Uncovering the Latent Structures of Crowd Labeling, In Proc. of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Ho Chi Minh City, Viet Nam, 2015. (PAKDD 2015)

• Ning Chen, Jun Zhu, Fei Xia, and Bo Zhang. Discriminative Relational Topic Models, IEEE Trans. on Pattern Analysis and Machine Intelligence, 37(5):973-986, 2015 (PAMI 2015)

• Jun Zhu and Wenbo Hu. Recent Advances in Bayesian Machie Learning, Journal of Computer Research and Development, 52(1):16-26, 2015. (in Chinese)

2014

• Jun Zhu and Eric P. Xing. Discriminative Training of Mixed Membership Models, Handbook of Mixed Membership Models and its Applications (Chap 18), edited by E.M. Airoldi, D.M. Blei, E.A. Erosheva, and S.E. Fienberg, 2014

• Jun Zhu, Ning Chen, and Eric P. Xing. Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs, Journal of Machine Learning Research, 15(May):1799-1847, 2014 (JMLR 2014)

• Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang. Gibbs Max-margin Topic Models with Data Augmentation, Journal of Machine Learning Research, 15(Mar):1073-1110, 2014 (JMLR 2014) [code]

• Yining Wang and Jun Zhu. Spectral Methods for Supervised Topic Models, In Proc. of Advances in Neural Information Processing Systems, Montreal, Canada, 2014 (NIPS 2014) [Appendix]

• Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, and Bo Zhang. Distributed Bayesian Posterior Sampling via Moment Sharing, In Proc. of Advances in Neural Information Processing Systems, Montreal, Canada, 2014 (NIPS 2014) [Code]

• Chaoyou Chen, Jun Zhu, and Xinhua Zhang. Robust Bayesian Max-Margin Clustering, In Proc. of Advances in Neural Information Processing Systems, Montreal, Canada, 2014 (NIPS 2014)

• Qi Lv and Jun Zhu. Revisit Long Short-Term Memory: An Optimization Perspective, In NIPS Workshop on Deep Learning and Representation Learning, Montreal, Canada, 2014 (NIPS-DL 2014)

• Shixia Liu, Xiting Wang, Jianfei Chen, Jun Zhu, and Baining Guo. TopicPanorama: a Full Picture of Relevant Topics, To Appear in Proc. of IEEE Visualization, Paris, France, 2014 (IEEE VIS 2014) [ project page] [ English Video] [ Chinese Video]

• Ning Chen, Jun Zhu, Jianfei Chen, and Bo Zhang. Dropout Training for Support Vector Machines, In Proc. of the 28th Conference on Artificial Intelligence, Quebec, Canada, 2014 (AAAI 2014, Oral)

• Yining Wang, and Jun Zhu. Small Variance Asymptotics for Dirichlet Process Mixtures of SVMs, In Proc. of the 28th Conference on Artificial Intelligence, Quebec, Canada, 2014 (AAAI 2014, Oral)

• Tianlin Shi, and Jun Zhu. Online Bayesian Passive Aggressive Learning, In Proc. of International Conference on Machine Learning, Beijing, China, 2014 (ICML 2014, Scoring top 18 in 1260+ submissions, recommended to JMLR fast track, Full Version) [Code]

• Shike Mei, Jun Zhu, and Jerry Zhu. Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models, In Proc. of International Conference on Machine Learning, Beijing, China, 2014 (ICML 2014)

• Chengtao Li, Jun Zhu, and Jianfei Chen. Bayesian Max-Margin Multitask Learning with Data Augmentation, In Proc. of International Conference on Machine Learning, Beijing, China, 2014 (ICML 2014)

• Aonan Zhang, Jun Zhu, and Bo Zhang. Max-margin Infinite Hidden Markov Models, In Proc. of International Conference on Machine Learning, Beijing, China, 2014. (ICML 2014)

• Renjie Liao, Jun Zhu, and Zengchang Qin. Nonparametric Bayesian Upstream Supervised Multi-Modal Topic Models, In Proc. of the 7th ACM Web Search and Data Mining Conference, New York, USA, 2014. (WSDM 2014)

2013

• Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, and Bo Zhang. Scalable Inference for Logistic-Normal Topic Models, In Proc. of Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, USA, 2013. (NIPS 2013) [Project Page]

• Ning Chen, Jun Zhu, Fucun Sun, and Bo Zhang. Learning Harmonium Models with Infinite Latent Features, IEEE Transactions on Neural Networks and Learning Systems, 2013. (IEEE TNNLS)

• Minjie Xu and Jun Zhu. Discriminative Infinite Latent Feature Models, IEEE China Summit and International Conference on Signal and Information Processing, Beijing, China, 2013. (ChinaSIP 2013; Note: An invited paper to summarize our recent work on learning discriminative infinite latent features for link prediction and matrix factorization..)

• Aonan Zhang, Jun Zhu, and Bo Zhang. Sparse Relational Topic Models for Document Networks, In Proc. of the 23rd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Prague, 2013. (ECML/PKDD 2013)

• Jun Zhu, Xun Zheng, Li Zhou, and Bo Zhang. Scalable Inference in Max-margin Topic Models, In Proc. of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Chicago, USA, 2013. (SIGKDD 2013)

• Jun Zhu, Xun Zheng, and Bo Zhang. Improved Bayesian Logistic Supervised Topic Models with Data Augmentation, In Proc. of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 2013. (ACL 2013)

• Minjie Xu, Jun Zhu, and Bo Zhang. Fast Max-Margin Matrix Factorization with Data Augmentation, In Proc. of the 30th International Conference on Machine Learning, Atlanta, USA, 2013. (ICML 2013, Oral) [Code]

• Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang. Gibbs Max-Margin Topic Models with Fast Sampling Algorithms, In Proc. of the 30th International Conference on Machine Learning, Atlanta, USA, 2013. (ICML 2013, Oral) [code]

• Ning Chen, Jun Zhu, Fei Xia, and Bo Zhang. Generalized Relational Topic Models with Data Augmentation, In Proc. of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, 2013. (IJCAI 2013, Oral Presentation)

• Aonan Zhang, Jun Zhu, and Bo Zhang. Sparse Online Topic Models, In Proc. of the 22nd International World Wide Web Conference, Rio de Janeiro, Brazil, 2013. (WWW 2013)

2012

• Cailiang Liu, Dong Wang, Jun Zhu, and Bo Zhang. Learning a Contextual Multi-thread Model for Movie/TV Scene Segmentation, To Appear in IEEE Transactions on Multimedia (TMM). (TMM 2012)

• Li-Jia Li, Jun Zhu, Hao Su, Eric P. Xing, and Li Fei-Fei. Multi-Level Structured Image Coding on High-Dimensional Image Representation, In Proc. of the 11th Asian Conference on Computer Vision (ACCV), Daejeon, Korea. (ACCV 2012)

• Minjie Xu, Jun Zhu, and Bo Zhang. Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction, In Proc. of Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, USA. (NIPS 2012) [code]

• Qixia Jiang, Jun Zhu, Maosong Sun, and Eric P. Xing. Monte Carlo Methods for Maximum Margin Supervised Topic Models, In Proc. of Advances in Neural Information Processing Systems (NIPS), Lake Tahoe, USA. (NIPS 2012)

• Jun Zhu, Amr Ahmed, and Eric P. Xing. MedLDA: Maximum Margin Supervised Topic Models, Journal of Machine Learning Research, 13(Aug):2237--2278, 2012. (JMLR 2012) [code]

• Jun Zhu. Max-Margin Nonparametric Latent Feature Models for Link Prediction, In Proc. of the 29th International Conference on Machine Learning, Edinburgh, Scotland, 2012. (ICML 2012)

• Yuandong Tian and Jun Zhu. Learning from Crowds in the Presence of Schools of Thought, In Proc. of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, 2012. (SIGKDD 2012)

• Ning Chen, Jun Zhu, Fuchun Sun, and Eric P. Xing. Large-margin Predictive Latent Subspace Learning for Multi-view Data Analysis, IEEE Trans. on PAMI, 34(12): 2365-2378 (2012)

2011

• Jun Zhu, Ning Chen, and Eric P. Xing. Infinite Latent SVM for Classification and Multi-task Learning, Advances in Neural Information Processing Systems (NIPS), Granada, Spain, 2011. (NIPS 2011)

• Chen Ning, Jun Zhu, and Fuchun Sun. Infinite Exponential Family Harmoniums, NIPS 2011 Workshop on Bayesian Nonparametrics: Hope or Hype?, Granada, Spain, 2011. (NIPS Workshop 2011)

• Jun Zhu, and Eric P. Xing. Sparse Topical Coding, In Proc. of 27th Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona, Spain, 2011. (UAI 2011) [Appendix] [code]

• Jun Zhu, Ni Lao, Ning Chen, and Eric P. Xing. Conditional Topical Coding: an Efficient Topic Model Conditioned on Rich Features, In Proc. of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011. (SIGKDD 2011).

• Jun Zhu, Ning Chen, and Eric P. Xing. Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines, In Proc. of the 28th International Conference on Machine Learning, Bellevue, Washington, USA, 2011. (ICML 2011)

2010

• Jun Zhu, Li-Jia Li, Li Fei-Fei and Eric P. Xing. Large Margin Learning of Upstream Scene Understanding Models, Advances in Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada, 2010. (NIPS 2010)

• Seunghak Lee, Jun Zhu and Eric P. Xing. Adaptive Multi-task Lasso: with Application to eQTLs Detection, Advances in Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada, 2010. (NIPS 2010)

• Ning Chen, Jun Zhu and Eric P. Xing. Predictive Subspace Learning for Multiview Data: a Large Margin Approach, Advances in Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada, 2010. (NIPS 2010)

• Ni Lao, Jun Zhu, Liu Liu, Yandong Liu and William Cohen. Efficient Relational Learning with Hidden Variable Detection, Advances in Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada, 2010. (NIPS 2010)

• Jun Zhu and Eric P. Xing. Conditional Topic Random Fields, In Proc. of the 27th International Conference on Machine Learning, Haifa, Israel, 2010. (ICML 2010)

• Ning Chen and Jun Zhu. MMH: Maximum Margin Supervised Harmoniums, ICML 2010 Workshop on Topic Models: Structure, Applications, Evaluation, and Extensions, Haifa, Israel, 2010. (ICML Workshop 2010)

• Jun Zhu, Ni Lao, and Eric P. Xing. Grafting-Light: Fast, Incremental Feature Selection and Structure Learning of Markov Random Fields, In Proc. of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC, USA, 2010. (SIGKDD 2010)

2009

• Jun Zhu. Maximum Entropy Discrimination Markov Networks: Theory and Applications, PhD Thesis, Tsinghua University, 2009. ( in Chinese: Awarded as Chinese Computer Federation (CCF) Distinguished Thesis and Tsinghua University Distinguished Thesis).

• Jun Zhu and Eric P. Xing. Maximum Entropy Discrimination Markov Networks, Journal of Machine Learning Research, 10(Nov):2531-2569, 2009. (JMLR 2009)

• Jun Zhu, and Eric P. Xing. On Primal and Dual Sparsity of Markov Networks, In Proc. of 26th International Conference on Machine Learning, Montreal, Canada, 2009. (ICML 2009)

• Jun Zhu, Amr Ahmed, and Eric P. Xing. MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification, In Proc. of 26th International Conference on Machine Learning, Montreal, Canada, 2009. (ICML 2009)

• Jun Zhu, Eric P. Xing, and Bo Zhang. Primal Sparse Max-Margin Markov Networks, In Proc. of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009. (SIGKDD 2009)

• Xiaolin Shi, Jun Zhu, Rui Cai, and Lei Zhang. User Grouping Behavior in Online Forums, In Proc. of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009. (SIGKDD 2009)

• Jun Zhu, Zaiqing Nie, Xiaojiang Liu, Bo Zhang, and Ji-Rong Wen. StatSnowball: a Statistical Approach to Extracting Entity Relationships, In Proc. of 18th International Word Wide Web Conference, Madrid, Spain, 2009. (WWW 2009) (research full paper)

• Jiangming Yang, Rui Cai, Yida Wang, Jun Zhu, Lei Zhang, and Wei-Ying Ma. Incorporating Site-Level Knowledge to Extract Structured Data from Web Forums, In Proc. of 18th International Word Wide Web Conference, Madrid, Spain, 2009. (WWW 2009) (research full paper)

• Jun Zhu, Zaiqing Nie, and Bo Zhang. Statistical Web Object Extraction, Invited Book Chapter in Encyclopedia of Data Warehousing and Mining, Second Edition, 2009.

2008

• Jun Zhu, Zaiqing Nie, Bo Zhang, and Ji-Rong Wen. Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction, Journal of Machine Learning Research, 9(Jul):1583--1614, 2008. (JMLR 2008)

• Jun Zhu, Eric P. Xing, and Bo Zhang. Partially Observed Maximum Entropy Discrimination Markov Networks, Advances in Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada, 2008. (NIPS 2008)

• Jun Zhu, Eric P. Xing, and Bo Zhang. Laplace Maximum Margin Markov Networks, In Proc. of the 25th International Conference on Machine Learning, Helsinki, Finland, 2008. (ICML 2008)

2007

• Jun Zhu, Zaiqing Nie, Bo Zhang, and Ji-Rong Wen. Dynamic Hierarchical Markov Random Fields and Their Application to Web Data Extraction, In Proc. of the 24th International Conference on Machine Learning, Corvalis, OR, USA, 2007. (ICML 2007)

• Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, and Hsiao-Wuen Hon. Webpage Understanding: an Integrated Approach, In Proc. of the 13rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, 2007. (SIGKDD 2007)

2006 & 2005

• Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, and Wei-Ying Ma. Simultaneous Record Detection and Attribute Labeling in Web Data Extraction, In Proc. of the 12nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, 2006. (SIGKDD 2006)

• Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, and Wei-Ying Ma. 2D Conditional Random Fields for Web Information Extraction, In Proc. of the 22nd International Conference on Machine Learning, Bonn, Germany, 2005. (ICML 2005)