Tsinghua University Sep 2014 – Jul 2019 PhD in Computer Science and Technology, supervised by Bo Zhang
Tsinghua University Sep 2010 – Jul 2014 BE in Institute for Interdisciplinary Information and Sciences (Yao Class)
Tsinghua University Jul 2019 – Jul 2021 Postdoctoral researcher, supervised by Jun Zhu Beijing, China
University of Amsterdam Sep 2017 – Sep 2018 PhD guest researcher, supervised by Max Welling Amsterdam, Netherlands
Deep generative models aim to learn a joint distribution on high-dimensional data. These models provide a principal and systematical way to capture the uncertainty in data and relieve the labeled-data eager problem of deep learning. Chongxuan’s research interests include: efficiently and stably learning deep generative models, applications of the models in environments with small data or limited supervision; understanding the behavior of the models in a theoretical perspective of consistency and stability.
National Natural Science Foundation – General Program (Nos. 62076145) Jan 2021 – Dec 2024
Study of Efficient and Convergent Learning and Inference Algorithms in Deep Generative Models Role: PI
Tsinghua-Huawei Large Granularity Long Term Corporation Project Sep 2019 – Aug 2022
Advanced Machine Learning Theory and Algorithm Role: sub-project co-leader
Chinese Postdoctoral Innovative Talent Support Program Jul 2019 – Jul 2021
Bayesian Deep Learning: Algorithms, Models and Applications Role: PI
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)
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 and Jun Zhu
Advances in Neural Information Processing Systems (NeurIPS)
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
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)
Fast Lossless Neural Compression with Integer-Only Discrete Flows
Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu and Bo Zhang
International Conference on Machine Learning (ICML)
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen and Jun Zhu
International Conference on Machine Learning (ICML)
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li† , Jiacheng Sun, Jun Zhu† and Bo Zhang († corresponding author)
International Conference on Machine Learning (ICML)
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† († corresponding author)
International Conference on Learning Representations (ICLR)
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li† , Jun Zhu† and Bo Zhang († corresponding author)
International Conference on Learning Representations (ICLR) (Outstanding Paper Award, acceptence rate < 0.16%)
2021
Triple Generative Adversarial Networks
Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu and Bo Zhang
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, preprint)
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Fan Bao*, Guoqiang Wu*, Chongxuan Li*, Jun Zhu and Bo Zhang (* equal contribution)
Advances in Neural Information Processing Systems (NeurIPS)
Rethinking Univariate Losses for Multi-Label Ranking: Consistency and Generalization
Guoqiang Wu*, Chongxuan Li*, Kun Xu and Jun Zhu （* equal contribution）
Advances in Neural Information Processing Systems (NeurIPS)
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† († corresponding author)
Conference on Computer Vision and Pattern Recognition (CVPR)
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu and Bo Zhang
International Conference on Machine Learning (ICML)
Implicit Normalizing Flows
Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang and Jun Zhu
International Conference on Learning Representations (ICLR) (Spotlight, acceptence rate < 5.6%)
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
Tsung Wei Tsai, Chongxuan Li and Jun Zhu
International Conference on Learning Representations (ICLR)
2020
Understanding and Stabilizing GANs’ Training Dynamics with Control Theory
Kun Xu, Chongxuan Li, Huanshu Wei, Jun Zhu and Bo Zhang
International Conference on Machine Learning (ICML)
Learning Implicit Generative Models by Teaching Explicit Ones
Kun Xu, Chao Du, Chongxuan Li, Jun Zhu and Bo Zhang
European Conference on Machine Learning (ECML)
Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon and Jun Zhu
Advances in Neural Information Processing Systems (NeurIPS)
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Fan Bao*, Chongxuan Li*, Kun Xu, Hang Su, Jun Zhu and Bo Zhang (* equal contribution)
Advances in Neural Information Processing Systems (NeurIPS)
To Relieve Your Headache of Training an MRF, Take AdVIL
Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu and Bo Zhang
International Conference on Learning Representations (ICLR)
2019
Multi-objects Generation with Amortized Structural Regularization
Kun Xu, Chongxuan Li, Jun Zhu and Bo Zhang
Advances in Neural Information Processing Systems (NeurIPS)
Conditional Graphical Generative Adversarial Networks
Chongxuan Li, Jun Zhu and Bo Zhang
Journal of Software (in Chinese)
2018
Collaborative Filtering with User-Item Co-Autoregressive Models
Chao Du,Chongxuan Li, Yin Zheng, Jun Zhu and Bo Zhang
Association for the Advancement of Artificial Intelligence (AAAI)
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Danyang Sun, Tongzheng Ren, Chongxuan Li, Jun Zhu, and Hang Su
International Joint Conferences on Artificial Intelligence (IJCAI)
Graphical Generative Adversarial Networks
Chongxuan Li, Max Welling, Jun Zhu and Bo Zhang
Advances in Neural Information Processing Systems (NeurIPS)
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Chongxuan Li, Jun Zhu and Bo Zhang
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
2017
Population Matching Discrepancy and Applications in Deep Learning
Jianfei Chen, Chongxuan Li, Yizhong Ru and Jun Zhu
Advances in Neural Information Processing Systems (NeurIPS)
Triple Generative Adversarial Nets
Chongxuan Li, Kun Xu, Jun Zhu and Bo Zhang
Advances in Neural Information Processing Systems (NeurIPS)
2016
Towards Better Analysis of Deep Convolutional Neural Networks
Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu
IEEE VAST, TVCG track
Learning to Generate with Memory
Chongxuan Li, Jun Zhu and Bo Zhang
International Conference on Machine Learning (ICML)
2015
Max-Margin Deep Generative Models
Chongxuan Li, Jun Zhu, Tianlin Shi and Bo Zhang
Advances in Neural Information Processing Systems (NeurIPS)
ICLR Outstanding Paper Award 2022
China Computer Federation (CCF) Distinguished Ph.D. Dissertation Award
Shuimu Tsinghua Scholar Program
Chinese Postdoctoral Innovative Talent Support Program
Microsoft Research Fellowship
Runner-up prize of the vizdoom AI competition 2017 @CIG
SPC: IJCAI
PC: UAI, AAAI
Reviewer: ICML, NeurIPS, ICLR, TPAMI, TNNLS
Email：chongxuanli@ruc.edu.cn
Website：https://zhenxuan00.github.io/