Yudong Chen -- Publications by topic

Non-convex optimization | Mixture of linear regressions | Graph clustering & community detection | Low-rank matrix/tensor estimation | Sparse & robust regresson | Wireless networks & signal processing | Intelligent transportation systems


Non-convex optimization for machine learning and statistics


Fast Algorithms for Robust PCA via Gradient Descent,
Xinyang Yi, Dohyung Park, Yudong Chen, 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, Martin J. Wainwright.
Preprint, 2015. [arXiv]



Mixture of linear regressions


Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes,
Dong Yin, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran
The 55th Annual Allerton Conference on Communication, Control, and Computing, 2017. [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, to appear, 2017. [arxiv]
Partial preliminary results appeared under the title "A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates" at the Conference on Learning Theory (COLT), 2014. [pdf]



Graph clustering and community detection


Exponential error rates of SDP for block models: Beyond Grothendieck's inequality,
Yingjie Fei, Yudong Chen
Preprint, 2017. [arxiv]

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 resutls appeared in ICML and NIPS.

Convexified Modularity Maximization for Degree-corrected Stochastic Block Models,
Yudong Chen, Xiaodong Li, and Jiaming Xu
Annals of Statistics, to appear, 2017. [arxiv] [web page and code]

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]

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]

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]
Partial results appeared at the International Conference on Machine Learning (ICML), 2014.

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]

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, March 2015. [pdf] [jmlr link] [arXiv]
Partial results appeared under the title "Breaking the Small Cluster Barrier of Graph Clustering" at the International Conference on Machine Learning (ICML), 2013.

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]
Preliminary results appeared under the title "Clustering Sparse Graphs" in Advances in Neural Information Processing Systems 25 (NIPS), 2012. 

Detecting Overlapping Temporal Community Structure in Time-Evolving Networks,
Yudong Chen, Vikas Kawadia, and Rahul Urgaonkar.
Technical Report, 2013. [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]
Partial preliminary results appeared at the International Conference on Machine Learning (ICML), 2011.



Low-rank matrix/tensor estimation


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] 

Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees,
Yudong Chen, Martin J. Wainwright.
Preprint, 2015. [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]
Partial results appeared at the International Conference on Machine Learning (ICML) 2014.

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

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]
Partial preliminary results appeared at the International Conference on Machine Learning (ICML), 2011.

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]
Partial preliminary results appeared at the International Symposium on Information Theory (ISIT), 2011.



Sparse and robust regression


Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent,
Yudong Chen, Lili Su, and Jiaming Xu
ACM SIGMETRICS, 2017. [arxiv]

Robust Sparse Regression under Adversarial Corruption,
Yudong Chen, Constantine Caramanis, and Shie Mannor.
The International Conference on Machine Learning (ICML), 2013. [pdf] [supplementary]
An earlier version of the paper with weaker results is available on [arXiv]

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery,
Yudong Chen and Constantine Caramanis.
The International Conference on Machine Learning (ICML), 2013. [pdf] [supplementary]
An earlier version of the paper with partial results is available on [arXiv].

Simple Algorithms for Sparse Linear Regression with Noisy and Missing Data,
Yudong Chen and Constantine Caramanis.
2012 IEEE Statistical Signal Processing Workshop (SSP'12).



Wireless networks and signal processing


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]
Partial preliminary results appeared at IEEE Globecom 2012.

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.



Intelligent transportation systems


Mining for Similarities in Urban Traffic Flow Using Wavelets,
Yudong Chen, Yi Zhang, Jianming Hu, and Li Li.
Proceedings of IEEE International Conference on Intelligent Transportation System (ITSC‘07), 2007.

Pattern Discovering of Regional Traffic Status with Self-Organizing Maps,
Yudong Chen, Yi Zhang and Jianming Hu.
Proceedings of IEEE International Conference on Intelligent Transportation System (ITSC’06), 2006.


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