Email:
chongxuanli [at] ruc (dot) edu (dot) cn
[Google Scholar]
[Github]
[Profiles-CN]
Research Topics
Deep Generative Models
Diffusion Models
Applications in AIGC
Theory of Generative Models
Generative AI for Science
I am a tenure-tack associate professor at Gaoling School of AI, Renmin University of China and I am leading the machine learning group at RUC
[Github] .
Before joining RUC, I was a Post Doc researcher in TSAIL at Tsinghua University, worked with Prof. Jun Zhu . I recieved my Ph.D. in the Department of Computer Science and Technology at Tsinghua University, supervised by Prof. Bo Zhang and 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.
Research Highlights
1. Efficient Sampling Algorithms for Diffusion Models
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]
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
[Paper]
[Code]
2. Large-Scale Generative Models and AIGC
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]
[Hugging Face]
[Colab]
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]
3. Theoretical Understanding of Generative Models
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]
Toward Understanding Generative Data Augmentation
Chenyu Zheng,
Guoqiang Wu,
Chongxuan Li †
Advances in Neural Information Processing Systems (NeurIPS ) 2023
[Paper]
[Code]
GSAI-ML Group
PhD Students
Min Zhao (with Jun Zhu)
Zhengyi Wang (with Jun Zhu)
Yong Zhong
Shen Nie
Zebin You
Rongzhen Wang
Kaiwen Xue
Chenyu Zheng
Undergraduate
Luxi Chen
Fengqi Zhu
Zihan Zhou
Alumni
Cheng Lu, PhD student (with Jun Zhu and Jianfei Chen), 2019-2024
FanBao, PhD student (with Jun Zhu), 2019-2024, now CTO at ShengShu
Kun Xu, PhD student (with Jun Zhu), 2016-2021, now at Citadel
Tsung Wei Tsai, master student (with Jun Zhu), 2019-2021, now at Goldman Sachs
Publications (* indicates equal contribution and † indicates correspondence.)
Preprint
ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing
Min Zhao,
Rongzhen Wang,
Fan Bao,
Chongxuan Li †,
Jun Zhu†
[Paper]
[Code]
[Demo]
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
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]
[Hugging Face]
[Colab]
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
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
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
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
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
Chinese Association for Artificial Intelligence , Wu Wenjun Award for Outstanding Youth in Artificial Intelligence , 2022
Alibaba Group , Outstanding Candidate Award of DAMO Academy Young Fellow , 2022
Beijing Municipal Science and Technology Commission , Beijing Nova Program , 2022
International Conference on Learning Representations , Outstanding Paper Award , 2022
Chinese Association for Artificial Intelligence , Wu Wenjun Natural Science Award for Artificial Intelligence (First Prize) , 2021
China Computer Federation , Distinguished PhD Dissertation Award , 2019
China Postdoctoral Science Foundation , Chinese Postdoctoral Innovative Talent Support Program , 2019
Microsoft Research Asia , MSRA Fellowship , 2017
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]
Diffusion Probabilistic Models and Fast Inference Algorithms by Estimating the Optimal Reverse Variance
VALSE Webinar , online, 2022-08-03
[Slides]
Stability and Generalization of (Gradient-Based) Bilevel Programming in Hyperparameter Optimization
Beijing Academy of Artificial Intelligence, BAAI-Live , online, 2021
[Slides]
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)
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
Area Chair or SPC: ICLR 2024, IJCAI 2022
Reviewer: ICML, NeurIPS, ICLR, UAI, IJCAI, AAAI, TPAMI, TCYB, TNNLS
Last updated on Aug. 2023. Special thanks to Chao Du and Jiajun Wu for the style files of the homepage.