Chao Du

Research Scientist, Sea AI Lab
Email: duchao0726 at gmail dot com

I am a research scientist at Sea AI Lab, Singapore. I finished my Ph.D. in 2019 at TSAIL Group in the Department of Computer Science and Technology at Tsinghua University, advised by Prof. Jun Zhu and Prof. Bo Zhang. And I received my B.Eng. from the Yao Class in Institute for Interdisciplinary Information Sciences at Tsinghua University in 2014.

My primary research focuses on probabilistic learning and inference in machine learning, with an emphasis on generative modeling. I am actively working on a diverse range of generative models (e.g., diffusion and flow-based). I'm also working on trustworthy machine learning, delving into understanding the vulnerabilities of machine learning algorithms from a probabilistic perspective.

We are currently hiring Research Interns working on the trustworthiness of generative models (large language models and diffusion models). Please do not hesitate to contact me if you are interested.

Publications (* denotes equal contribution; † denotes correspondence.) [Google Scholar]

  • On Evaluating Adversarial Robustness of Large Vision-Language Models
    Yunqing Zhao*, Tianyu Pang*†, Chao Du†, Xiao Yang, Chongxuan Li, Ngai-man Cheung†, Min Lin
    Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
    [Paper] [Codes] [Project Page]
  • Gaussian Mixture Solvers for Diffusion Models
    Hanzhong Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du†, Chongxuan Li†
    Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
    [Paper] [Codes]
  • On Calibrating Diffusion Probabilistic Models
    Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng
    Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
    [Paper] [Codes]
  • Efficient Diffusion Policies For Offline Reinforcement Learning
    Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, Shuicheng Yan
    Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
    [Paper] [Codes]
  • Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
    Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin
    International Conference on Machine Learning (ICML), Hawaii, USA, 2023
    [Paper] [Codes] [Slides] [Poster]
  • Better Diffusion Models Further Improve Adversarial Training
    Zekai Wang*, Tianyu Pang*, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan
    International Conference on Machine Learning (ICML), Hawaii, USA, 2023
    [Paper] [Codes]
  • Bag of Tricks for Training Data Extraction from Language Models
    Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan
    International Conference on Machine Learning (ICML), Hawaii, USA, 2023
    [Paper] [Codes]
  • Exploring Model Dynamics for Accumulative Poisoning Discovery
    Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han
    International Conference on Machine Learning (ICML), Hawaii, USA, 2023
    [Paper] [Codes]
  • Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
    Yunqing Zhao, Chao Du, Milad Abdollahzadeh, Tianyu Pang, Min Lin, Shuicheng Yan, Ngai-man Cheung
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023
    [Paper] [Codes] [Project Page]
  • Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning
    Haozhe Wang, Chao Du, Panyan Fang, Li He, Liang Wang, Bo Zheng
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), Long Beach, USA, 2023
    [Paper]
  • ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
    Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan, Xuming He, Liang Wang, Bo Zheng
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), Washington DC, USA, 2022
    [Paper]
  • Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
    Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), Virtual Conference, 2021
    [Paper] [Codes]
  • A Bayesian Approach for Subset Selection in Contextual Bandits
    Jialian Li, Chao Du, Jun Zhu
    Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), Virtual Conference, 2021
    [Paper]
  • Learning Implicit Generative Models by Teaching Density Estimators
    Kun Xu*, Chao Du*, Chongxuan Li, Jun Zhu, Bo Zhang
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Ghent, Belgium, 2020
    [Paper] [Codes]
  • Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
    Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu
    International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020
    [Paper] [Codes]
  • To Relieve Your Headache of Training an MRF, Take AdVIL
    Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang
    International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020
    [Paper] [Codes]
  • Improving Adversarial Robustness via Promoting Ensemble Diversity
    Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu
    International Conference on Machine Learning (ICML), Long Beach, USA, 2019
    [Paper] [Codes]
  • Towards Robust Detection of Adversarial Examples
    Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
    Annual Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018
    [Paper] [Codes]
  • Max-Mahalanobis Linear Discriminant Analysis Networks
    Tianyu Pang, Chao Du, Jun Zhu
    International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018
    [Paper] [Codes]
  • Collaborative Filtering with User-Item Co-Autoregressive Models
    Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang
    Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018
    [Paper] [Codes]
  • Learning Deep Generative Models with Doubly Stochastic Gradient MCMC
    Chao Du, Jun Zhu, Bo Zhang
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018, 29(7): 3084-3096.
    [Paper] [Codes]
  • Composite Quantization for Approximate Nearest Neighbor Search
    Ting Zhang, Chao Du, Jingdong Wang
    International Conference on Machine Learning (ICML), Beijing, China, 2014
    [Paper]

Workshop Papers and Preprints

  • Learning Implicit Generative Models by Teaching Explicit Ones
    Chao Du*, Kun Xu*, Chongxuan Li, Jun Zhu, Bo Zhang
    ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, 2018.
    [Paper] [Codes]
  • Inner Product Similarity Search using Compositional Codes
    Chao Du, Jingdong Wang
    [Paper]

Competitions

Services

  • Reviewer: ICML, NeurIPS, ICLR, CVPR, SIGKDD, TPAMI, TNNLS

Contact Me

duchao0726 at gmail dot com