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-track 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 focuses on deep generative models, which aim to characterize the distribution of the input data and simulate the underlying world. Our mission is to understand the power and limitation of existing models, design scalable and effecient next-generation models, and develop principled and effective algorithms for AIGC applicaitons.
Selected Publications
Full Publications
Diffusion language models
Large Language Diffusion Models
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li †
Scaling up Masked Diffusion Models on Text
Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, Chongxuan Li †
International Conference on Learning Representations (
ICLR ), 2025
[Paper] [Code]
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 †
International Conference on Learning Representations (
ICLR ), 2025
[Paper] [Code]
3D world models
FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis
Luxi Chen,
Zihan Zhou,
Min Zhao,
Yikai Wang†,
Ge Zhang,
Wenhao Huang,
Hao Sun,
Ji-Rong Wen,
Chongxuan Li †
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†
Diffusion transformers
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Min Zhao,
Guande He,
Yixiao Chen,
Hongzhou Zhu,
Chongxuan Li †,
Jun Zhu†
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]
Fast sampling 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
[Paper] [Code] Oral, Outstanding Paper Award (7/3391)
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
[Paper] [Code]
Preprint
Large Language Diffusion Models
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li †
[Paper] [Code] [Demo]
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Min Zhao,
Guande He,
Yixiao Chen,
Hongzhou Zhu,
Chongxuan Li †,
Jun Zhu†
[Paper] [Code] [Demo]
FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis
Luxi Chen,
Zihan Zhou,
Min Zhao,
Yikai Wang†,
Ge Zhang,
Wenhao Huang,
Hao Sun,
Ji-Rong Wen,
Chongxuan Li †
[Paper] [Code] [Demo]
Concat-ID: Towards Universal Identity-Preserving Video Synthesis
Yong Zhong,
Zhuoyi Yang,
Jiayan Teng,
Xiaotao Gu,
Chongxuan Li †
[Paper] [Code] [Demo]
Effective and Efficient Masked Image Generation Models
Zebin You,
Jingyang Ou,
Xiaolu Zhang,
Jun Hu,
Jun Zhou,
Chongxuan Li †
[Paper] [Code]
Real-time Identity Defenses against Malicious Personalization of Diffusion Models
HanZhong Guo, Shen Nie, Chao Du, Tianyu Pang, Hao Sun†, Chongxuan Li †
[Paper] [Code]
A Theory for Conditional Generative Modeling on Multiple Data Sources
Rongzhen Wang, Yan Zhang, Chenyu Zheng, Chongxuan Li †, Guoqiang Wu†
[Paper] [Code]
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]
2025
Are Image Distributions Indistinguishable to Humans Indistinguishable to Classifiers?
Zebin You, Xinyu Zhang, Hanzhong Guo, Jingdong Wang, Chongxuan Li †
The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR ), 2025
[Paper]
Scaling up Masked Diffusion Models on Text
Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, Chongxuan Li †
International Conference on Learning Representations (ICLR ), 2025
[Paper] [Code]
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 †
International Conference on Learning Representations (ICLR ), 2025
[Paper] [Code]
Improving Long-Text Alignment for Text-to-Image Diffusion Models
Luping Liu, Chao Du, Tianyu Pang, Zehan Wang, Chongxuan Li , Dong Xu
International Conference on Learning Representations (ICLR ), 2025
[Paper] [Code]
On Memorization in Diffusion Models
Xiangming Gu, Chao Du, Tianyu Pang,
Chongxuan Li , Min Lin, Ye Wang
Transactions on Machine Learning Research (TMLR ), 2025
[Paper] [Code]
ControlVideo: Conditional Control for Text-driven Video Editing and Beyond
Min Zhao, Rongzhen Wang, Fan Bao,
Chongxuan Li †,
Jun Zhu†
Science China Information Sciences (SCIS ), 2025
[Paper] [Code]
2024
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability
Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li †
Advances in Neural Information Processing Systems (NeurIPS ), 2024
[Paper] [Code]
Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization
Rongzhen Wang, Chenyu Zheng, Guoqiang Wu, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li †
Advances in Neural Information Processing Systems (NeurIPS ), 2024
[Paper]
Identifying and Solving Conditional Image Leakage in Image-to-Video Generation
Min Zhao, Hongzhou Zhu, Chendong Xiang, Kaiwen Zheng, Chongxuan Li †, Jun Zhu†
Advances in Neural Information Processing Systems (NeurIPS ), 2024
[Paper] [Code] [Demo]
Graph Diffusion Policy Optimization
Yijing Liu, Chao Du, Tianyu Pang, Chongxuan Li , Wei Chen, Min Lin
Advances in Neural Information Processing Systems (NeurIPS ), 2024
[Paper] [Code]
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
[Paper] [Code]
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] [Code]
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
[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
Oral, Outstanding Paper Award (7/3391)
[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]
GSAI-ML Group
PhD Students
Zhengyi Wang (with Jun Zhu and Hang Su)
Yong Zhong
Shen Nie
Zebin You
Rongzhen Wang
Kaiwen Xue
Chenyu Zheng
Luxi Chen
Fengqi Zhu
Undergraduate
Jingyang Ou
Sitong Chen
Bokai Yan
Alumni
Min Zhao, PhD (with Jun Zhu), 2020-2024, Shuimu Program, now Postdoc at Tsinghua University
Cheng Lu, PhD (with Jun Zhu and Jianfei Chen), 2019-2023, ByteDance Scholarship, now research scientist at OpenAI
Fan Bao, PhD (with Jun Zhu), 2019-2023, ByteDance Scholarship, now CTO at ShengShu
Kun Xu, PhD (with Jun Zhu), 2016-2021, now at Citadel
Hanzhong Guo, master, 2022-2024, now PhD at HKU
Tsung Wei Tsai, master (with Jun Zhu), 2019-2021, now at Goldman Sachs
Selected Awards
ACM China , ACM SIGAI China Rising Star , 2023
Chinese Association for Artificial Intelligence , Wu Wenjun Award for Outstanding Youth in AI , 2022
Alibaba Group DAMO Academy Young Fellow Nomination , 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 Award for Natural Science (First Class) , 2021
China PostdoctOral Presentation Science Foundation , Chinese Postdoctoral Innovative Talent Support Program , 2019
China Computer Federation Distinguished PhD Dissertation Award , 2019
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
Deep Generative Models: Principles and Applications (Online) Available at BiliBili 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
Associate Editor: IEEE TPAMI
责任编委: 软件学报
Area Chair: NeurIPS, ICLR, ACM MM
Reviewer: Nature Communications
Last updated on Feb. 2025. Special thanks to Chao Du and Jiajun Wu for the style files of the homepage.
