Yudong Chen    

News

In Fall 2023 I am teaching CS 540 Introduction to Artificial Intelligence.

Our work on RL with low-rank structure received the Outstanding Student Paper Award from ACM SIGMETRICS 2023. Congratulation to my student Tyler Sam (co-advised with Christina Lee Yu)!

I received the NSF CAREER Award for our work on nonconvex and nonsmooth optimization.

Congratulations to my student Lijun Ding for winning the 2019 Student Paper Prize of INFORMS Optimization Society.

Our work on hidden integrality of SDP relaxation finished 2nd Place in the 2018 INFORMS George Nicholson Student Paper Competition. Congratulations to my student Yingjie (Tom) Fei!

Bio

I am an Associate Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Previously I was a tenured associate professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. My research interests include machine learning, reinforcement learning, high-dimensional and robust statistics, and optimization. Some of the topics that I am interested in are: reinforcement learning with low-dimensional structures, non-convex statistical algorithms, mixture problems, robust matrix completion and PCA, graph clustering and community detection in networks, sparse recovery and compressed sensing, large-scale learning and optimization, and computational and statistical tradeoffs.

I obtained my Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin, advised by Constantine Caramanis. I was a postdoc in the EECS department at the University of California, Berkeley hosted by Martin J. Wainwright. I received my B.S. and M.S. from Tsinghua University.

Publications (chronological order)

(Also see my Google Scholar page)

Clustering Without an Eigengap
Matthew Zurek, Yudong Chen
Preprint, 2023. [arxiv]

Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen, Qiaomin Xie
Preprint, 2023. [arxiv]

Tackling Unbounded State Spaces in Continuing Task Reinforcement Learning
Brahma S. Pavse, Yudong Chen, Qiaomin Xie, Josiah P. Hanna
Preprint, 2023. [arxiv]

Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Yige Hong, Qiaomin Xie, Yudong Chen, and Weina Wang
Preprint, 2023. [arxiv]

Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure
Xumei Xi, Christina Lee Yu, and Yudong Chen
Preprint, 2023. [arxiv]
Partial preliminary results appears in the 3rd NeurIPS Offline RL Workshop, 2022.

Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
Dongyan (Lucy) Huo, Yudong Chen, and Qiaomin Xie
ACM SIGMETRICS, 2023. [arxiv]

Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure
Tyler Sam, Yudong Chen, and Christina Lee Yu
ACM SIGMETRICS, 2023. [arxiv] [acm link]
Outstanding Student Paper, ACM SIGMETRICS 2023.

Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization
Liwei Jiang, Yudong Chen, and Lijun Ding
SIAM Journal on Mathematics of Data Science (SIMODS), vol. 5, no. 3, pp. 723-744, 2023. [arxiv] [siam link]

Entry-Specific Bounds for Low-Rank Matrix Completion Under Highly Non-Uniform Sampling
Xumei Xi, Christina Lee Yu, and Yudong Chen
IEEE International Symposium on Information Theory (ISIT), 2023.

A Geometric Approach to k-means
Jiazhen Hong, Wei Qian, Yudong Chen, and Yuqian Zhang
Preprint, 2022. [arxiv]

Clustering Heterogeneous Financial Networks
Hamed Amini, Yudong Chen, Andreea Minca, and Xin Qian
Mathematical Finance, 2022. [link]

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
Fanghui Liu, Xiaolin Huang, Yudong Chen, and Johan Suykens
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7975-7988, 2022. [ieee link]

Hidden Integrality and Semi-random Robustness of SDP Relaxation for Sub-Gaussian Mixture Models
Yingjie Fei and Yudong Chen
Mathematics of Operations Research, vol. 47, no. 3, pp. 2464-2493, 2022. [MOR link]
Partial preliminary results appeared in COLT 2018.

Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
Yingjie Fei, Zhuoran Yang, Yudong Chen, and Zhaoran Wang
Neural Information Processing Systems Conference (NeurIPS), 2021. [arxiv]

Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, and Zhihui Zhu
Neural Information Processing Systems Conference (NeurIPS), 2021. [arxiv]

Likelihood Landscape and Local Minima Structures of Gaussian Mixture Models
Yudong Chen, Dogyoon Song, Xumei Xi, and Yuqian Zhang
Preprint, 2021. [arxiv]

Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle
Lijun Ding, Yuqian Zhang, and Yudong Chen
Preprint, 2021. [arxiv]

Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu, Xiaolin Huang, Yudong Chen, and Johan A.K. Suykens
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), to appear, 2021. [arxiv]

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, and Qiaomin Xie
Neural Information Processing Systems Conference (NeurIPS), 2020. (Spotlight) [arxiv]

Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium
Qiaomin Xie, Yudong Chen, Zhaoran Wang, and Zhuoran Yang
Mathematics of Operations Research, to appear, 2022. [arxiv] [MOR link]
Partial preliminary results appeared in Conference on Learning Theory (COLT), 2020.

Structures of Spurious Local Minima in k-means
Wei Qian, Yuqian Zhang, and Yudong Chen
IEEE Transactions on Information Theory, to appear, 2021. [arxiv]

Achieving the Bayes Error Rate in Synchronization and Block Models by SDP, Robustly
Yingjie Fei and Yudong Chen.
IEEE Transactions on Information Theory, vol. 66, no. 6, pp. 3929-3953, 2020. [arxiv] [ieee link]
Partial preliminary results appeared in COLT. 

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
Fanghui Liu, Xiaolin Huang, Yudong Chen, Jie Yang, and Johan Suykens
Association for the Advancement of Artificial Intelligence Conference (AAAI), 2020. [arxiv]

Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Wei Qian, Yuqian Zhang, and Yudong Chen.
Neural Information Processing Systems Conference (NeurIPS), 2019. [arxiv]

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Jicong Fan, Lijun Ding, Yudong Chen, and Madeleine Udell
Neural Information Processing Systems Conference (NeurIPS), 2019. [arxiv]

Low-rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence
Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Diaz, Lijun Ding, and Dmitriy Drusvyatskiy.
Foundations of Computational Mathematics, to appear, 2019. [arxiv]

Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, and Damek Davis.
Conference on Learning Theory (COLT), 2019. [arxiv]

Achieving the Bayes Error Rate in Stochastic Block Model by SDP, Robustly
Yingjie Fei and Yudong Chen.
Conference on Learning Theory (COLT), 2019. [colt pdf]

Convex Relaxation Methods for Community Detection
Xiaodong Li, Yudong Chen, and Jiaming Xu.
Statistical Science, vol. 36, no. 1, pp. 2-15, 2021. [arxiv]

Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett.
International Conference on Machine Learning (ICML), 2019 (long talk). [arxiv]

The Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis
Lijun Ding and Yudong Chen.
IEEE Transactions on Information Theory, to appear, 2019. [arxiv] [ieee link]

Hidden Integrality of SDP Relaxation for Sub-Gaussian Mixture Models
Yingjie Fei and Yudong Chen.
Conference on Learning Theory (COLT), 2018. [arxiv]
2nd Place, 2018 INFORMS George Nicholson Student Paper Competition.

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett.
International Conference on Machine Learning (ICML), 2018. [arxiv]

Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation
Yudong Chen and Yuejie Chi.
IEEE Signal Processing Magazine, vol. 35, no. 4, pp. 14-31, 2018. [arxiv] [ieee link]

Exponential error rates of SDP for block models: Beyond Grothendieck's inequality
Yingjie Fei and Yudong Chen.
IEEE Transactions on Information Theory, vol. 65, no. 1, pp. 551-571, 2019. [arxiv] [ieee link]

Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
Yudong Chen, Lili Su, and Jiaming Xu.
ACM SIGMETRICS, 2018. [paper link] [arxiv]

Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, and Shuicheng Yan
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 42, no. 4, pp. 925-938, 2020. [arxiv] [ieee link]

Convex and Nonconvex Formulations for Mixed Regression with Two Components: Minimax Optimal Rates
Yudong Chen, Xinyang Yi, and Constantine Caramanis.
IEEE Transactions on Information Theory, vol. 64, no. 3, pp. 1738-1766, 2018. [ieee link]
Partial preliminary results appeared in COLT and [arxiv]

Convexified Modularity Maximization for Degree-corrected Stochastic Block Models
Yudong Chen, Xiaodong Li, and Jiaming Xu.
Annals of Statistics, vol. 46, no. 4, pp. 1573-1602, 2018. [arxiv] [code and webpage]

Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes
Dong Yin, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran.
IEEE Transactions on Information Theory, vol. 65, no. 3, pp. 1430-1451, 2019. [arxiv] [ieee link]
Partial preliminary results appeared in the 55th Annual Allerton Conference on Communication, Control, and Computing, 2017.

Clustering from General Pairwise Observations with Applications to Time-varying Graphs
Shiau Hong Lim, Yudong Chen, and Huan Xu.
Journal of Machine Learning Research (JMLR), 18(49), 1-47, 2017. [pdf] [jmlr link]
Partial preliminary results appeared in ICML and NIPS.

Fast Algorithms for Robust PCA via Gradient Descent
Xinyang Yi, Dohyung Park, Yudong Chen, and Constantine Caramanis.
Neural Information Processing Systems Conference (NIPS), 2016. [arxiv] [webpage] [code]

Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees
Yudong Chen, and Martin J. Wainwright.
Preprint, 2015. [arXiv]

Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, and Shuicheng Yan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [pdf]

A Convex Optimization Framework for Bi-Clustering
Shiau Hong Lim, Yudong Chen, and Huan Xu.
International Conference on Machine Learning (ICML), 2015. [pdf] [supplementary] [icml link]

Matrix Completion with Column Manipulation: Near-Optimal Sample-Robustness-Rank Tradeoffs
Yudong Chen, Huan Xu, Constantine Caramanis, and Sujay Sanghavi.
IEEE Transactions on Information Theory, vol. 62, no. 1, pp. 503-526, 2016. [arxiv] [ieee link]

Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
Yudong Chen and Jiaming Xu.
Journal of Machine Learning Research (JMLR), vol. 17, no. 27, pp. 1-57, 2016. [pdf] [arXiv]

Completing Any Low-Rank Matrix, Provably
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, and Rachel Ward.
Journal of Machine Learnng Research (JMLR), vol. 16, pp. 2999-3034, 2015. [pdf] [jmlr link]

Incoherence-Optimal Matrix Completion
Yudong Chen.
IEEE Transactions on Information Theory, vol. 61, no. 5, pp. 2909-2923, 2015. [ieee link] [arXiv]

Iterative and Active Graph Clustering Using Trace Norm Minimization Without Cluster Size Constraints
Nir Ailon, Yudong Chen, and Huan Xu.
Journal of Machine Learning Research (JMLR), vol. 16, pp. 450-490, 2015. [pdf] [jmlr link] [arXiv]

Improved Graph Clustering
Yudong Chen, Sujay Sanghavi, and Huan Xu.
IEEE Transactions on Information Theory, vol. 60, no. 10, pp. 6440–6455, 2014. [ieee link] [arXiv]

Clustering Partially Observed Graphs via Convex Optimization
Yudong Chen, Ali Jalali, Sujay Sanghavi, and Huan Xu.
Journal of Machine Learning Research (JMLR), vol. 15, pp. 2213-2238, 2014. [pdf] [arXiv]

Clustering from Labels and Time-Varying Graphs
Shiau Hong Lim, Yudong Chen, and Huan Xu.
Neural Information Processing Systems Conference (NIPS), 2014 (Spotlight). [pdf] [supplementary] [nips link]

Weighted Graph Clustering with Non-uniform Uncertainties
Yudong Chen, Shiau Hong Lim, and Huan Xu.
International Conference on Machine Learning (ICML), 2014. [pdf] [supplementary] [icml link]

Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting
Yudong Chen and Jiaming Xu.
International Conference on Machine Learning (ICML), 2014.

Coherent Matrix Completion
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, and Rachel Ward.
International Conference on Machine Learning (ICML), 2014.

A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates
Yudong Chen, Xinyang Yi, and Constantine Caramanis.
Conference on Learning Theory (COLT), 2014. [arxiv] [colt pdf]

Low-rank Matrix Recovery from Errors and Erasures
Yudong Chen, Ali Jalali, Sujay Sanghavi, and Constantine Caramanis.
IEEE Transactions on Information Theory, vol. 59, no. 7, pp. 4324-4337, 2013. [ieee link] [arXiv]

Detecting Overlapping Temporal Community Structure in Time-Evolving Networks
Yudong Chen, Vikas Kawadia, and Rahul Urgaonkar.
Technical Report, 2013. [arXiv]

User Association for Load Balancing in Heterogeneous Cellular Networks
Qiaoyang Ye, Beiyu Rong, Yudong Chen, Mazin Al-Shalash, Constantine Caramanis, and Jeffrey G. Andrews.
IEEE Transactions on Wireless Communications, vol. 12, no. 6, pp. 2706-2716, 2013. [ieee link] [arXiv]

Breaking the Small Cluster Barrier of Graph Clustering
Nir Ailon, Yudong Chen, and Huan Xu.
International Conference on Machine Learning (ICML), 2013.

Robust Sparse Regression under Adversarial Corruption
Yudong Chen, Constantine Caramanis, and Shie Mannor.
International Conference on Machine Learning (ICML), 2013. [pdf] [supplementary]

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery
Yudong Chen and Constantine Caramanis.
International Conference on Machine Learning (ICML), 2013. [pdf] [supplementary]

Clustering Sparse Graphs
Yudong Chen, Sujay Sanghavi, and Huan Xu.
In Advances in Neural Information Processing Systems 25 (NIPS), 2012. 

Towards an Optimal User Association in Heterogeneous Cellular Networks
Qiaoyang Ye, Beiyu Rong, Yudong Chen, Mazin Al-Shalash, Constantine Caramanis, and Jeffrey G. Andrews.
IEEE Globecom, 2012.

Low-rank Matrix Recovery from Errors and Erasures
Yudong Chen, Ali Jalali, Sujay Sanghavi, and Constantine Caramanis.
International Symposium on Information Theory (ISIT), 2011.

Clustering Partially Observed Graphs via Convex Optimization
Ali Jalali, Yudong Chen, Sujay Sanghavi, and Huan Xu.
International Conference on Machine Learning (ICML), 2011.

Robust Matrix Completion with Corrupted Columns
Yudong, Chen, Huan Xu, Constantine Caramanis, and Sujay Sanghavi.
International Conference on Machine Learning (ICML), 2011.

Quantization Errors of Uniformly Quantized fGn and fBm Signals
Zhiheng Li, Yudong Chen, Li Li, and Yi Zhang.
IEEE Signal Processing Letters, vol. 16, no. 12, 1059-1062, 2009. [arXiv]

PCA Based Hurst Exponent Estimator for fBm Signals under Disturbances
Li Li, Jianming Hu, Yudong Chen, and Yi Zhang.
IEEE Transactions on Signal Processing, vol. 57, no. 7, 2840-2846, 2009.

Teaching

UW-Madison

CS 726 Nonlinear Optimization I (Spring 2023, course page)

CS 839 Probability and Learning in High Dimension (Spring 2022, course page)

CS 540 Introduction to Artificial Intelligience (Fall 2021, course page)

Cornell

ENGRD 2700 Basic Engineering Probability and Statistics (Fall 2019, course page; Spring 2019)

ORIE 4740 Statistical Data Mining I (Spring 2021, course page; Fall 2018, course page; Spring 2018; Spring 2017; Spring 2016)

ORIE 6700 Statistical Principles (Fall 2020, course page; Fall 2017, course page; Fall 2016; Fall 2015)

ORIE 7790 High Dimensional Probability and Statistics (Spring 2020, course  page)

Group Members

Ph.D Students:

Matthew Zurek, Ph.D. Student at UW-Madison CS

Lucy Huo, Ph.D. Student at Cornell ORIE (co-advised with Jim Dai)

Tyler Sam, Ph.D. Student at Cornell ORIE (co-advised with Christina Lee Yu)

Xumei Xi, Ph.D. Student at Cornell ORIE (co-advised with Christina Lee Yu)

Group Alumni:

Lijun Ding (Cornell Ph.D. co-advised with Madeleine Udell): Postdoc at University of Washington and University of Wisconsin-Madison

Yingjie (Tom) Fei (Cornell Ph.D.): Postdoc at Northwestern University

Wei Qian (Cornell Ph.D.): Munich Re

Yuqian Zhang (Cornell Postdoc): Assistant Professor at Rugters ECE