Yi Zhou - RankMe

121 papers A* 25A 5B 4Misc 4Journal 63Unranked 20

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization. Trans. Mach. Learn. Res. Yufeng Yang, Erin E. Tripp, Yifan Sun, Shaofeng Zou, Yi Zhou
2025	J	`jnl`	Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. Trans. Mach. Learn. Res. Qi Zhang, Yi Zhou, Shaofeng Zou
2025	J	`jnl`	Deep learning of PDE correction and mesh adaption without automatic differentiation. Mach. Learn. Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou
2025	—	`conf`	Dynamic Power Control in Multi-Cell Networks: A Robust Multi-Agent Reinforcement Learning Approach. IEEECONF Xinrui Ye, Joohyun Cho, Yi Zhou, Rong-Rong Chen
2025	—	`conf`	Enhancing Graph Partitioning with Reinforcement Learning-based Initialization. HPEC Chedi Morchdi, Cheng-Hsiang Chiu, Wan-Luan Lee, Tsung-Wei Huang, Yi Zhou
2025	J	`jnl`	Nested Stochastic Gradient Descent for (Generalized) Sinkhorn Distance-Regularized Distributionally Robust Optimization. CoRR Yufeng Yang, Yi Zhou, Zhaosong Lu
2025	—	`conf`	Optimizing CUDA Graph Scheduling with Reinforcement Learning - A Case Study in SSTA Propagation. MLCAD Cheng-Hsiang Chiu, Chedi Morchdi, Chih-Chun Chang, Cunxi Yu, Yi Zhou, Tsung-Wei Huang
2025	J	`jnl`	Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality. CoRR Shaocong Ma, Ziyi Chen, Yi Zhou, Heng Huang
2025	J	`jnl`	Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality. Trans. Mach. Learn. Res. Shaocong Ma, Ziyi Chen, Yi Zhou, Heng Huang
2025	A*	`conf`	Revisiting Large-Scale Non-convex Distributionally Robust Optimization. ICLR Qi Zhang, Yi Zhou, Simon Khan, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou
2024	B	`conf`	A Resource-efficient Task Scheduling System using Reinforcement Learning : Invited Paper. ASPDAC Chedi Morchdi, Cheng-Hsiang Chiu, Yi Zhou, Tsung-Wei Huang
2024	J	`jnl`	Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. IEEE Trans. Autom. Control. Yan Zhang, Yi Zhou, Kaiyi Ji, Yi Shen, Michael M. Zavlanos
2024	J	`jnl`	Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. CoRR Qi Zhang, Yi Zhou, Shaofeng Zou
2024	J	`jnl`	End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. CoRR Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou
2024	J	`jnl`	Finite-time error bounds for Greedy-GQ. Mach. Learn. Yue Wang, Yi Zhou, Shaofeng Zou
2024	A*	`conf`	Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. AAAI Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou
2024	J	`jnl`	Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. CoRR Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou
2024	A*	`conf`	Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. ICML Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou
2024	J	`jnl`	Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. CoRR Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou
2024	A*	`conf`	On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning. ICLR Ziyi Chen, Yi Zhou, Heng Huang
2024	A*	`conf`	On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback. ICLR Ziwei Guan, Yi Zhou, Yingbin Liang
2024	—	`conf`	Reinforcement Learning-Generated Topological Order for Dynamic Task Graph Scheduling. HPEC Cheng-Hsiang Chiu, Chedi Morchdi, Yi Zhou, Boyang Zhang, Che Chang, Tsung-Wei Huang
2023	J	`jnl`	A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization. Trans. Mach. Learn. Res. Ziyi Chen, Zhengyang Hu, Qunwei Li, Zhe Wang, Yi Zhou
2023	J	`jnl`	Assisted Learning for Organizations with Limited Imbalanced Data. Trans. Mach. Learn. Res. Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou
2023	B	`conf`	Assisted Unsupervised Domain Adaptation. ISIT Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou
2023	J	`jnl`	Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty. J. Mach. Learn. Res. Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou
2023	A*	`conf`	Edge-cloud Collaborative Learning with Federated and Centralized Features. SIGIR Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu
2023	J	`jnl`	Edge-cloud Collaborative Learning with Federated and Centralized Features. CoRR Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu
2023	—	`conf`	Exploring Gradient Oscillation in Deep Neural Network Training. Allerton Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang
2023	A*	`conf`	Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. ICML Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu
2023	Misc	`conf`	Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication. ACSSC Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen
2023	A*	`conf`	Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback. COLT Ziwei Guan, Yi Zhou, Yingbin Liang
2023	—	`conf`	Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. TAG-ML Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang
2022	J	`jnl`	A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. CoRR Ziyi Chen, Bhavya Kailkhura, Yi Zhou
2022	J	`jnl`	A new one-point residual-feedback oracle for black-box learning and control. Autom. Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos
2022	B	`conf`	Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. ISIT Ziyi Chen, Shaocong Ma, Yi Zhou
2022	J	`jnl`	Data Sampling Affects the Complexity of Online SGD over Dependent Data. CoRR Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang
2022	A	`conf`	Data sampling affects the complexity of online SGD over dependent data. UAI Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang
2022	—	`conf`	Data-Driven Robust Multi-Agent Reinforcement Learning. MLSP Yudan Wang, Yue Wang, Yi Zhou, Alvaro Velasquez, Shaofeng Zou
2022	A*	`conf`	Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach. NeurIPS Ziyi Chen, Shaocong Ma, Yi Zhou
2022	J	`jnl`	Finite-Time Error Bounds for Greedy-GQ. CoRR Yue Wang, Yi Zhou, Shaofeng Zou
2022	J	`jnl`	Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities. Trans. Mach. Learn. Res. Ziyi Chen, Yi Zhou, Rong-Rong Chen
2022	A*	`conf`	Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game. ICLR Ziyi Chen, Shaocong Ma, Yi Zhou
2022	A*	`conf`	Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. ICML Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou
2022	J	`jnl`	Understanding generalization error of SGD in nonconvex optimization. Mach. Learn. Yi Zhou, Yingbin Liang, Huishuai Zhang
2021	J	`jnl`	Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. CoRR Ziyi Chen, Shaocong Ma, Yi Zhou
2021	J	`jnl`	Assisted Learning for Organizations with Limited Data. CoRR Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou
2021	B	`conf`	Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. MASS Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou
2021	J	`jnl`	Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. CoRR Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou
2021	—	`conf`	Communication-Free Two-Stage Multi-Agent DDPG under Partial States and Observations. ACSCC Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen
2021	J	`jnl`	Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent. CoRR Ziyi Chen, Qunwei Li, Yi Zhou
2021	J	`jnl`	Finite-Sample Analysis for Two Time-scale Non-linear TDC with General Smooth Function Approximation. CoRR Yue Wang, Shaofeng Zou, Yi Zhou
2021	A*	`conf`	Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. ICLR Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou
2021	J	`jnl`	Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. CoRR Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou
2021	J	`jnl`	MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney
2021	J	`jnl`	Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity. CoRR Ziyi Chen, Yi Zhou, Rongrong Chen
2021	—	`conf`	Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexity. ACSCC Ziyi Chen, Yi Zhou, Rongrong Chen
2021	A*	`conf`	Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation. NeurIPS Yue Wang, Shaofeng Zou, Yi Zhou
2021	A*	`conf`	Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. ICLR Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang
2021	J	`jnl`	Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. CoRR Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang
2021	J	`jnl`	Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. CoRR Ziyi Chen, Yi Zhou, Rongrong Chen, Shaofeng Zou
2021	J	`jnl`	Understanding Estimation and Generalization Error of Generative Adversarial Networks. IEEE Trans. Inf. Theory Kaiyi Ji, Yi Zhou, Yingbin Liang
2020	A*	`conf`	A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. NeurIPS Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer
2020	J	`jnl`	Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. CoRR Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos
2020	—	`conf`	FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. IEEE BigData Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura
2020	J	`jnl`	FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. CoRR Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura
2020	A*	`conf`	History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. ICML Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang
2020	J	`jnl`	Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback. CoRR Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos
2020	J	`jnl`	Momentum with Variance Reduction for Nonconvex Composition Optimization. CoRR Ziyi Chen, Yi Zhou
2020	—	`conf`	Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. IEEE BigData Cheng Chen, Junjie Yang, Yi Zhou
2020	J	`jnl`	Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. CoRR Cheng Chen, Junjie Yang, Yi Zhou
2020	Misc	`conf`	Perception-Distortion Trade-Off with Restricted Boltzmann Machines. ICASSP Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh
2020	A*	`conf`	Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. IJCAI Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh
2020	J	`jnl`	Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. CoRR Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh
2020	A*	`conf`	Reanalysis of Variance Reduced Temporal Difference Learning. ICLR Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang
2020	J	`jnl`	Reanalysis of Variance Reduced Temporal Difference Learning. CoRR Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang
2020	Misc	`conf`	Supervised Encoding for Discrete Representation Learning. ICASSP Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh
2020	A*	`conf`	Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. ICML Shaocong Ma, Yi Zhou
2020	J	`jnl`	Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. CoRR Shaocong Ma, Yi Zhou
2020	A*	`conf`	Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. NeurIPS Shaocong Ma, Yi Zhou, Shaofeng Zou
2020	J	`jnl`	Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. CoRR Shaocong Ma, Yi Zhou, Shaofeng Zou
2019	J	`jnl`	A note on inexact gradient and Hessian conditions for cubic regularized Newton's method. Oper. Res. Lett. Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
2019	J	`jnl`	A simple convergence analysis of Bregman proximal gradient algorithm. Comput. Optim. Appl. Yi Zhou, Yingbin Liang, Lixin Shen
2019	A	`conf`	Cubic Regularization with Momentum for Nonconvex Optimization. UAI Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
2019	—	`conf`	Distributed SGD Generalizes Well Under Asynchrony. Allerton Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang
2019	J	`jnl`	Distributed SGD Generalizes Well Under Asynchrony. CoRR Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang
2019	J	`jnl`	Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation. CoRR Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang
2019	A*	`conf`	Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. ICML Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang
2019	J	`jnl`	Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. CoRR Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang
2019	—	`conf`	Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation. MLSP Yi Zhou, Yi Feng, Vahid Tarokh, Vadas Gintautas, Jessee McClelland, Denis Garagic
2019	—	`conf`	Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. IEEE BigData Yi Feng, Yi Zhou, Vahid Tarokh
2019	—	`conf`	SGD Converges to Global Minimum in Deep Learning via Star-convex Path. ICLR (Poster) Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh
2019	J	`jnl`	SGD Converges to Global Minimum in Deep Learning via Star-convex Path. CoRR Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh
2019	A*	`conf`	SpiderBoost and Momentum: Faster Variance Reduction Algorithms. NeurIPS Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
2019	A	`conf`	Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. AISTATS Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
2019	J	`jnl`	Supervised Encoding for Discrete Representation Learning. CoRR Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh
2019	—	`conf`	Toward Understanding the Impact of Staleness in Distributed Machine Learning. ICLR (Poster) Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing
2018	J	`jnl`	A Note on Inexact Condition for Cubic Regularized Newton's Method. CoRR Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
2018	A*	`conf`	Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. NeurIPS Yi Zhou, Zhe Wang, Yingbin Liang
2018	J	`jnl`	Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. CoRR Yi Zhou, Zhe Wang, Yingbin Liang
2018	J	`jnl`	Convergence of SGD in Learning ReLU Models with Separable Data. CoRR Tengyu Xu, Yi Zhou, Kaiyi Ji, Yingbin Liang
2018	—	`conf`	Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. ICLR (Poster) Yi Zhou, Yingbin Liang
2018	J	`jnl`	Cubic Regularization with Momentum for Nonconvex Optimization. CoRR Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
2018	J	`jnl`	Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing
2018	J	`jnl`	Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization. CoRR Yi Zhou, Yingbin Liang, Huishuai Zhang
2018	J	`jnl`	MR-GAN: Manifold Regularized Generative Adversarial Networks. CoRR Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney
2018	J	`jnl`	Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. CoRR Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
2018	J	`jnl`	SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization. CoRR Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
2018	J	`jnl`	Toward Understanding the Impact of Staleness in Distributed Machine Learning. CoRR Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing
2017	J	`jnl`	Characterization of Gradient Dominance and Regularity Conditions for Neural Networks. CoRR Yi Zhou, Yingbin Liang
2017	A*	`conf`	Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. ICML Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney
2017	J	`jnl`	Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. CoRR Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney
2017	J	`jnl`	Critical Points of Neural Networks: Analytical Forms and Landscape Properties. CoRR Yi Zhou, Yingbin Liang
2017	Misc	`conf`	Demixing sparse signals via convex optimization. ICASSP Yi Zhou, Yingbin Liang
2017	A*	`conf`	Learning Latent Space Models with Angular Constraints. ICML Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing
2016	—	`conf`	Geometrical properties and accelerated gradient solvers of non-convex phase retrieval. Allerton Yi Zhou, Huishuai Zhang, Yingbin Liang
2016	A	`conf`	Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing
2016	—	`conf`	On Compressive orthonormal Sensing. Allerton Yi Zhou, Huishuai Zhang, Yingbin Liang
2016	A	`conf`	On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing
2015	—	`conf`	Analysis of Robust PCA via Local Incoherence. NIPS Huishuai Zhang, Yi Zhou, Yingbin Liang
2015	J	`jnl`	Distributed Machine Learning via Sufficient Factor Broadcasting. CoRR Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing