Chongxuan Li（李崇轩）

Associate Professor
Gaoling School of AI, Renmin University of China
Email: chongxuanli [at] ruc (dot) edu (dot) cn

[Google Scholar] [Profiles-CN] [Interview-CN]

Machine Learning Group (GSAI-ML)
[GSAI-ML Github] [GSAI-ML 知乎]

I am a tenure-tack associate professor at Gaoling School of AI, Renmin University of China. I recieved my Ph.D. in the Department of Computer Science and Technology at Tsinghua University, supervised by Prof. Bo Zhang and Prof. Jun Zhu. During my Ph.D., I had spent a great year at AMLab, working with Prof. Max Welling. I received the B.E. degree from Institute for Interdiscriplinary Information Sciences at Tsinghua University in 2014.

My group studies deep generative models, which aim to characterize the distribution of input data and simulate the underlying world. Our research attempts to understand the power and limitation of such models, design the next generation of generative models that are easy to scale up on complex data, and develop principled and effective algorithms for AIGC applicaitons. See representative projects as follows.

My course on deep generative models is available at bilibili.

GSAI-ML Group

PhD Students
  • Zhengyi Wang (with Jun Zhu)
  • Yong Zhong
  • Shen Nie
  • Zebin You
  • Rongzhen Wang
  • Kaiwen Xue
  • Chenyu Zheng
  • Luxi Chen
  • Fengqi Zhu
Master Students
  • Zihan Zhou
Undergraduate
  • Jingyang Ou
  • Sitong Chen

Alumni

  • Min Zhao, PhD student (with Jun Zhu), 2020-2024, now Postdoc (Shuimu Scholarship) at Tsinghua University
  • Cheng Lu, PhD student (with Jun Zhu and Jianfei Chen), 2019-2023, ByteDance Scholarship, now at OpenAI
  • Fan Bao, PhD student (with Jun Zhu), 2019-2023, ByteDance Scholarship, now CTO at ShengShu
  • Kun Xu, PhD student (with Jun Zhu), 2016-2021, now at Citadel
  • Hanzhong Guo, master student, 2022-2024, now PhD student at HKU
  • Tsung Wei Tsai, master student (with Jun Zhu), 2019-2021, now at Goldman Sachs

    • Publications (* indicates equal contribution and † indicates correspondence.)

      Preprint
    • Identifying and Solving Conditional Image Leakage in Image-to-Video Generation
      Min Zhao, Hongzhou Zhu, Chendong Xiang, Kaiwen Zheng, Chongxuan Li†, Jun Zhu†
      [Paper] [Code] [Demo]
    • Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
      Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, Chongxuan Li†
      [Paper]
    • On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
      Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li†
      [Paper]
    • MicroDreamer: Zero-shot 3D Generation in ~20 Seconds by Score-based Iterative Reconstruction
      Luxi Chen, Zhengyi Wang, Zihan Zhou, Tingting Gao, Hang Su, Jun Zhu, Chongxuan Li†
      [Paper] [Code]
    • Are Image Distributions Indistinguishable to Humans Indistinguishable to Classifiers?
      Zebin You, Xinyu Zhang, Hanzhong Guo, Jingdong Wang, Chongxuan Li†
      [Paper]
    • On Memorization in Diffusion Models
      Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang
      [Paper] [Code]
      • 2024
    • PoseCrafter: One-Shot Personalized Video Synthesis Following Flexible Poses
      Yong Zhong, Min Zhao, Zebin You, Xiaofeng Yu, Changwang Zhang, Chongxuan Li†
      European Conference on Computer Vision (ECCV), 2024
      [Paper] [Demo]
    • CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
      Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu
      European Conference on Computer Vision (ECCV), 2024
      [Paper] [Project] [Code] [Demo]
    • Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
      Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li†
      International Conference on Machine Learning (ICML), 2024
      [Paper] [Code]
    • EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
      Yang Zhang, Wenbing Huang, Zhewei Wei, Ye Yuan, Chongxuan Li
      International Conference on Machine Learning (ICML), 2024
      Oral Presentation
      [Paper]
    • The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing
      Shen Nie, Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, Chongxuan Li†
      International Conference on Learning Representations (ICLR), 2024
      [Paper] [Code] [Demo]
    • BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference
      Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li†, Zhijie Deng†
      International Conference on Learning Representations (ICLR), 2024
      [Paper]
      • 2023
    • ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
      Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li†, Hang Su, Jun Zhu†
      Advances in Neural Information Processing Systems (NeurIPS), 2023
      Spotlight
      [Paper] [Code] [Demo]
    • Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
      Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li†, Jun Zhu
      Advances in Neural Information Processing Systems (NeurIPS), 2023
      Spotlight
      [Paper] [Code] [Demo]
    • Toward Understanding Generative Data Augmentation
      Chenyu Zheng, Guoqiang Wu, Chongxuan Li†
      Advances in Neural Information Processing Systems (NeurIPS), 2023
      [Paper] [Code]
    • Gaussian Mixture Solvers for Diffusion Models
      Hanzhong Allan Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du†, Chongxuan Li†
      Advances in Neural Information Processing Systems (NeurIPS), 2023
      [Paper] [Code]
    • On Evaluating Adversarial Robustness of Large Vision-Language Models
      Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngni-Man Cheung, Min Lin
      Advances in Neural Information Processing Systems (NeurIPS), 2023
      [Paper] [Code]
    • Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
      Yudeng Lin, Qingtian Zhang, Bin Gao, Jianshi Tang, Peng Yao, Chongxuan Li, Shiyu Huang, Zhengwu Liu, Ying Zhou, Yuyi Liu, Wenqiang Zhang, Jun Zhu, He Qian, Huaqiang Wu
      Nature Machine Intelligence (NMI), 2023
      [Paper]
    • MissDiff: Training Diffusion Models on Tabular Data with Missing Values
      Yidong Ouyang, Liyan Xie, Chongxuan Li, Guang Cheng
      Structured Probabilistic Inference & Generative Modeling Workshop @ International Conference on Machine Learning (SPIGM@ICML), 2023
      [Paper] [Long version]
    • One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
      Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu
      International Conference on Machine Learning (ICML), 2023
      [Paper] [Code]
    • Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
      Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li†, Jun Zhu
      International Conference on Machine Learning (ICML), 2023
      [Paper] [Code]
    • Towards Understanding Generalization of Macro-AUC in Multi-label Learning
      Guoqiang Wu, Chongxuan Li, Yilong Yin
      International Conference on Machine Learning (ICML), 2023
    • Exact Energy-Guided Diffusion Sampling via Contrastive Energy Prediction
      Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu
      International Conference on Machine Learning (ICML), 2023
    • All are Worth Words: a ViT Backbone for Diffusion Models
      Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li†, Hang Su, Jun Zhu†
      The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
      [Paper] [Code]
    • Equivariant Energy-Guided SDE for Inverse Molecular Design
      Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li†, Jun Zhu†
      International Conference on Learning Representations (ICLR), 2023
    • Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
      Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen†, Chongxuan Li†
      International Conference on Learning Representations (ICLR), 2023
      • 2022
    • Why Are Conditional Generative Models Better Than Unconditional Ones?
      Fan Bao, Chongxuan Li†, Jiacheng Sun, Jun Zhu†
      Score-based Model workshop @ Advances in Neural Information Processing Systems (SBM@NeurIPS), 2022
    • EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
      Min Zhao, Fan Bao, Chongxuan Li†, Jun Zhu†
      Advances in Neural Information Processing Systems (NeurIPS), 2022
    • DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
      Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
      Advances in Neural Information Processing Systems (NeurIPS), 2022
      Oral Presentation
      [Paper] [Code]
    • Fast Lossless Neural Compression with Integer-Only Discrete Flows
      Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang
      International Conference on Machine Learning (ICML), 2022
    • Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
      Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen, Jun Zhu
      International Conference on Machine Learning (ICML), 2022
    • Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
      Fan Bao, Chongxuan Li†, Jiacheng Sun, Jun Zhu†, Bo Zhang
      International Conference on Machine Learning (ICML), 2022
      [Code]
    • Memory Replay with Data Compression for Continual Learning
      Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li†, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong†, Jun Zhu†
      International Conference on Learning Representations (ICLR), 2022
      [Paper]
    • Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
      Fan Bao, Chongxuan Li†, Jun Zhu†, Bo Zhang
      International Conference on Learning Representations (ICLR), 2022
      Outstanding Paper Award
      [Paper] [Code]
    • Probabilistic Neural-Symbolic Models with Inductive Posterior Constraints
      Ke Su, Hang Su, Chongxuan Li, Jun Zhu, Bo Zhang
      IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
    • Deep reinforcement learning with credit assignment for combinatorial optimization
      Dong Yan, Jiayi Weng, Shiyu Huang, Chongxuan Li, Yichi Zhou, Hang Su, Jun Zhu
      Pattern Recognition, 2022 (Runner-up Prize of IEEE VIZDoom RL Competition 2017)
      [Paper]
      • 2021
    • Triple Generative Adversarial Networks
      Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang.
      IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
      [Paper] [Code]
    • Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
      Fan Bao*, Guoqiang Wu*, Chongxuan Li*, Jun Zhu, Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2021
      [Paper]
    • Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
      Guoqiang Wu*, Chongxuan Li*, Kun Xu, Jun Zhu
      Advances in Neural Information Processing Systems (NeurIPS), 2021
      [Paper]
    • ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
      Liyuan Wang, Kuo Yang, Chongxuan Li†, Lanqing Hong, Zhenguo Li, Jun Zhu†
      The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2021
      [Paper]
    • Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
      Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang
      International Conference on Machine Learning (ICML), 2021
      [Paper] [Code]
    • Implicit Normalizing Flows
      Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu
      International Conference on Learning Representations (ICLR), 2021
      Spotlight
      [Paper] [Code]
    • MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
      Tsung Wei Tsai, Chongxuan Li, Jun Zhu
      International Conference on Learning Representations (ICLR), 2021
      [Paper] [Code]
      • 2020
    • Efficient Learning of Generative Models via Finite-Difference Score Matching
      Tianyu Pang*, Kun Xu*, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
      Advances in Neural Information Processing Systems (NeurIPS), 2020
      [Paper] [Code]
    • Bi-level Score Matching for Learning Energy-based Latent Variable Models
      Fan Bao*, Chongxuan Li*, Hang Su, Jun Zhu, Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2020
      [Paper] [Code]
    • 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), 2020
      [Paper] [Code]
    • Understanding and Stabilizing GANs' Training Dynamics with Control Theory
      Kun Xu, Chongxuan Li, Huanshu Wei, Jun Zhu, Bo Zhang
      International Conference on Machine Learning (ICML), 2020
      [Paper] [Code]
    • 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), 2020
      [Paper]
      • 2019
    • Conditional Graphical Generative Adversarial Networks
      Chongxuan Li, Jun Zhu, Bo Zhang
      Journal of Software (in Chinese), 2019
    • Multi-objects Generation with Amortized Structural Regularization
      Kun Xu, Chongxuan Li, Jun Zhu and Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2019
      [Paper]
      • 2018
    • Max-Margin Deep Generative Models for (Semi-)Supervised Learning
      Chongxuan Li, Jun Zhu, Bo Zhang.
      IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
      [Paper] [Code]
    • Graphical Generative Adversarial Networks
      Chongxuan Li, Max Welling, Jun Zhu and Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2018
      [Paper] [Code]
    • Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
      Danyang Sun, Tongzheng Ren, Chongxuan Li, Jun Zhu, Hang Su, Bo Zhang
      International Joint Conferences on Artificial Intelligence (IJCAI), 2018
      [Paper]
    • 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), 2018
      [Paper] [Code]
      • 2017
    • Triple Generative Adversarial Nets
      Chongxuan Li, Kun Xu, Jun Zhu, Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2017
      [Paper] [Code]
    • Population Matching Discrepancy and Applications in Deep Learning
      Jianfei Chen, Chongxuan Li, Yizhong Ru, Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2017
      [Paper]
      • 2016
    • Towards Better Analysis of Deep Convolutional Neural Networks
      Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu
      IEEE VAST 2016, TVCG track, 2016
      [Paper] [Demo]
    • Learning to Generate with Memory
      Chongxuan Li, Jun Zhu, Bo Zhang
      International Conference on Machine Learning (ICML), 2016
      [Paper] [Code]
      • 2015
    • Max-Margin Deep Generative Models
      Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang
      Advances in Neural Information Processing Systems (NeurIPS), 2015
      [Paper] [Code]

    Selected Awards

    Talk

    • Diffusion Models and AIGC
      Tutorial in Vision and Learning Seminar (VALSE) @ Wuxi, 2023
      [Slides in Chinese]
    • Diffusion Probabilistic Models: Foundations, Fast Inference and Controllable Generation
      Forum of Techniques in Foundation Model, Beijing Academy of Artificial Intelligence BAAI, online, 2022-12-17
      [Slides] [Blog]
    • Deep Generative Models: Representation, Learning and Inference
      Tutorial of unsupervised learning in Vision and Learning Seminar (VALSE) @ Hangzhou, 2021
      A short version is also presented in MLNLP community, online, 2021
      [Slides]

    Teaching

    • Probability and Randomized Algorithms (Graduate, Autumn, since 2021)
    • Deep Generative Models: Principles and Applications (Graduate, Spring, since 2022) Online version
    • Probabilistic Graphical Models: Principles and Applications (Undergraduate, Spring, since 2023)

    Book Chapter

    • 可解释人工智能导论（第二章 贝叶斯方法），电子工业出版社，2022年4月

    Service

    • Associate Editor: TPAMI
    • Area Chair : NeurIPS 2024, ICLR 2024, ACM MM 2024
    • SPC: IJCAI 2021
    Last updated on Jun. 2024. Special thanks to Chao Du and Jiajun Wu for the style files of the homepage.