Yudong Chen    

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Assistant Professor
School of Operations Research and Information Engineering
Cornell University
223 Frank H.T. Rhodes Hall, Ithaca, NY 14853
Phone: (607) 255-0698   Fax: (607) 255-9129
yudong.chen at cornell dot edu

[Bio]  [Publications (chronological order) (by topic) (by type)]  [Teaching]

News

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!

In Spring 2019 I will teach ENGRD 2700 Basic Engineering Probability and Statistics. Please contact Cindy Jay (cjh6@cornell.edu) for pre-enrollement issues.

Bio

I am an assistant professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University (also a graduate field member of Computer Science, Electrical & Computer Engineering, Statistics, and Applied Mathemathcs). My research interests include machine learning, high-dimensional and robust statistics, and optimization. Some of the topics that I am interested in are: sparse recovery and compressed sensing, robust matrix completion and PCA, graph clustering and community detection in networks, mixture problems, large-scale learning and optimization, computational and statstistical tradeoffs, and non-convex statistical algorithms.

I obtained my Ph.D. in Electrical and Computer Engineering in 2013 from The University of Texas at Austin, advised by Constantine Caramanis. From 2013 to 2015 I was a postdoc in the EECS department at the University of California, Berkeley hosted by Martin J. Wainwright. In 2014 and 2015 I was a visiting scholar at the National University of Singapore. I received my B.S. and M.S. from Tsinghua University. I worked as an intern at Raytheon BBN, IBM and Siemens.

Publications (chronological order)

(See publications by type, publications by topic, and my Google Scholar page)

Convex Relaxation Methods for Community Detection
Xiaodong Li, Yudong Chen, and Jiaming Xu.
Preprint, 2018. [arxiv]

Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett.
Preprint, 2018. [arxiv]

The Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis
Lijun Ding and Yudong Chen.
Preprint, 2018. [arxiv]

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, to appear, 2018. [arxiv]

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
Preprint, 2018. [arxiv]

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] [web page and code]

Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes
Dong Yin, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran.
IEEE Transactions on Information Theory, to appear, 2018. [arxiv]
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] [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

ENGRD 2700 Basic Engineering Probability and Statistics (Spring 2019)

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

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