Yudong Chen -- Publications by topic
Reinforcement learning
| Robust
distributed learning
| Non-convex optimization
| Mixture of linear regressions
| Graph clustering &
community detection | Low-rank
matrix/tensor estimation | Sparse
& robust regresson | Wireless
networks, signal processing & kernel methods | Intelligent
transportation systemsBias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
Dongyan (Lucy) Huo, Yudong
Chen, and Qiaomin Xie
Preprint, 2022. [arxiv]
Overcoming the Long Horizon
Barrier for Sample-Efficient Reinforcement Learning with
Latent Low-Rank Structure
Tyler
Sam, Yudong
Chen, and Christina Lee Yu
Preprint, 2022. [arxiv]
Best
Poster Award, ACM SIGMETRICS/IFIP Performance 2022.
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]
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
Conference on Learning Theory (COLT), 2020. [arxiv]
Robust distributed learning and optimization
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]
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]
Distributed
Statistical Machine Learning in Adversarial Settings: Byzantine
Gradient Descent
Yudong Chen, Lili Su, and Jiaming Xu.
ACM SIGMETRICS, 2018. [paper
link] [arxiv]
Non-convex optimization for machine learning and statistics
Algorithmic Regularization in
Model-free Overparametrized Asymmetric Matrix Factorization
Liwei Jiang, Yudong Chen, and Lijun Ding
Preprint, 2022. [arxiv]
A Geometric Approach to k-means
Jiazhen Hong, Wei Qian, Yudong Chen, and
Yuqian Zhang
Preprint, 2022. [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 and Xumei Xi
Preprint, 2020. [arxiv]
Structures of Spurious Local
Minima in k-means
Wei Qian, Yuqian Zhang, and Yudong Chen
IEEE Transactions on Information Theory, to appear, 2021. [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]
The
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis
Lijun
Ding and Yudong Chen.
IEEE Transactions on Information Theory, 2019. [arxiv] [ieee
link]
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]
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]
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]
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.
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 under the title "A
Convex
Formulation for Mixed Regression with Two Components: Minimax Optimal
Rates" at the Conference on Learning Theory (COLT), 2014.
[pdf] [arxiv]
Graph clustering and community detection
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 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]
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.
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]
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, vol. 46, no. 4, pp. 1573-1602, 2018. [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 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]
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.
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, signal processing and kernel methods
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]
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]
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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