Analysis of Robust PCA via Local Incoherence. - Huishuai Zhang; Yi Zhou 017; Yingbin Liang (NIPS, 2015)

Distributed Machine Learning via Sufficient Factor Broadcasting. - Pengtao Xie; Jin Kyu Kim; Yi Zhou 017; Qirong Ho; Abhimanu Kumar; Yaoliang Yu; Eric P. Xing (CoRR, 2015)

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. - Yi Zhou 017; Yaoliang Yu; Wei Dai 003; Yingbin Liang; Eric P. Xing (AISTATS, 2016)

On Compressive orthonormal Sensing. - Yi Zhou 017; Huishuai Zhang; Yingbin Liang (Allerton, 2016)

Geometrical properties and accelerated gradient solvers of non-convex phase retrieval. - Yi Zhou 017; Huishuai Zhang; Yingbin Liang (Allerton, 2016)

Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. - Pengtao Xie; Jin Kyu Kim; Yi Zhou 017; Qirong Ho; Abhimanu Kumar; Yaoliang Yu; Eric P. Xing (UAI, 2016)

Demixing sparse signals via convex optimization. - Yi Zhou 017; Yingbin Liang (ICASSP, 2017)

Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. - Qunwei Li; Yi Zhou 017; Yingbin Liang; Pramod K. Varshney (ICML, 2017)

Learning Latent Space Models with Angular Constraints. - Pengtao Xie; Yuntian Deng; Yi Zhou 017; Abhimanu Kumar; Yaoliang Yu; James Zou 001; Eric P. Xing (ICML, 2017)

Characterization of Gradient Dominance and Regularity Conditions for Neural Networks. - Yi Zhou 017; Yingbin Liang (CoRR, 2017)

Critical Points of Neural Networks: Analytical Forms and Landscape Properties. - Yi Zhou 017; Yingbin Liang (CoRR, 2017)

Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. - Yi Zhou 017; Yingbin Liang (ICLR (Poster), 2018)

Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. - Yi Zhou 017; Zhe Wang 021; Yingbin Liang (NeurIPS, 2018)

Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization. - Yi Zhou 017; Yingbin Liang; Huishuai Zhang (CoRR, 2018)

Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (CoRR, 2018)

Convergence of SGD in Learning ReLU Models with Separable Data. - Tengyu Xu; Yi Zhou 017; Kaiyi Ji; Yingbin Liang (CoRR, 2018)

A Note on Inexact Condition for Cubic Regularized Newton's Method. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (CoRR, 2018)

SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization. - Zhe Wang 021; Kaiyi Ji; Yi Zhou 017; Yingbin Liang; Vahid Tarokh (CoRR, 2018)

MR-GAN: Manifold Regularized Generative Adversarial Networks. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Yi Zhou 017; Yingbin Liang; Pramod K. Varshney (CoRR, 2018)

Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. - Yi Zhou 017; Yingbin Liang; Yaoliang Yu; Wei Dai 003; Eric P. Xing (J. Mach. Learn. Res., 2018)

Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (AISTATS, 2019)

Distributed SGD Generalizes Well Under Asynchrony. - Jayanth Regatti; Gaurav Tendolkar; Yi Zhou 017; Abhishek Gupta 002; Yingbin Liang (Allerton, 2019)

Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. - Yi Feng; Yi Zhou 017; Vahid Tarokh (IEEE BigData, 2019)

Toward Understanding the Impact of Staleness in Distributed Machine Learning. - Wei Dai 003; Yi Zhou 017; Nanqing Dong; Hao Zhang 025; Eric P. Xing (ICLR (Poster), 2019)

SGD Converges to Global Minimum in Deep Learning via Star-convex Path. - Yi Zhou 017; Junjie Yang; Huishuai Zhang; Yingbin Liang; Vahid Tarokh (ICLR (Poster), 2019)

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. - Kaiyi Ji; Zhe Wang 021; Yi Zhou 017; Yingbin Liang (ICML, 2019)

Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation. - Yi Zhou 017; Yi Feng; Vahid Tarokh; Vadas Gintautas; Jessee McClelland; Denis Garagic (MLSP, 2019)

SpiderBoost and Momentum: Faster Variance Reduction Algorithms. - Zhe Wang 021; Kaiyi Ji; Yi Zhou 017; Yingbin Liang; Vahid Tarokh (NeurIPS, 2019)

Cubic Regularization with Momentum for Nonconvex Optimization. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (UAI, 2019)

A simple convergence analysis of Bregman proximal gradient algorithm. - Yi Zhou 017; Yingbin Liang; Lixin Shen (Comput. Optim. Appl., 2019)

Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation. - Kaiyi Ji; Zhe Wang 021; Yi Zhou 017; Yingbin Liang (CoRR, 2019)

A note on inexact gradient and Hessian conditions for cubic regularized Newton's method. - Zhe Wang 021; Yi Zhou 017; Yingbin Liang; Guanghui Lan (Oper. Res. Lett., 2019)

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. - Cheng Chen; Ziyi Chen 002; Yi Zhou 017; Bhavya Kailkhura (IEEE BigData, 2020)

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. - Cheng Chen; Junjie Yang; Yi Zhou 017 (IEEE BigData, 2020)

Perception-Distortion Trade-Off with Restricted Boltzmann Machines. - Chris Cannella; Jie Ding 002; Mohammadreza Soltani; Yi Zhou 017; Vahid Tarokh (ICASSP, 2020)

Supervised Encoding for Discrete Representation Learning. - Cat P. Le; Yi Zhou 017; Jie Ding 002; Vahid Tarokh (ICASSP, 2020)

Reanalysis of Variance Reduced Temporal Difference Learning. - Tengyu Xu; Zhe Wang 021; Yi Zhou 017; Yingbin Liang (ICLR, 2020)

History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. - Kaiyi Ji; Zhe Wang 021; Bowen Weng; Yi Zhou 017; Wei Zhang 013; Yingbin Liang (ICML, 2020)

Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. - Shaocong Ma; Yi Zhou 017 (ICML, 2020)

Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. - Yi Zhou 017; Zhe Wang 021; Kaiyi Ji; Yingbin Liang; Vahid Tarokh (IJCAI, 2020)

Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. - Shaocong Ma; Yi Zhou 017; Shaofeng Zou (NeurIPS, 2020)

Momentum with Variance Reduction for Nonconvex Composition Optimization. - Ziyi Chen 002; Yi Zhou 017 (CoRR, 2020)

Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Michael M. Zavlanos (CoRR, 2020)

Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Michael M. Zavlanos (CoRR, 2020)

Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexity. - Ziyi Chen 002; Yi Zhou 017; Rongrong Chen (ACSCC, 2021)

Communication-Free Two-Stage Multi-Agent DDPG under Partial States and Observations. - Joohyun Cho; Mingxi Liu; Yi Zhou 017; Rong-Rong Chen (ACSCC, 2021)

Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. - Ziyi Chen 002; Yi Zhou 017; Tengyu Xu; Yingbin Liang (ICLR, 2021)

Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. - Shaocong Ma; Ziyi Chen 002; Yi Zhou 017; Shaofeng Zou (ICLR, 2021)

Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. - Cheng Chen; Bhavya Kailkhura; Ryan A. Goldhahn; Yi Zhou 017 (MASS, 2021)

Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation. - Yue Wang 068; Shaofeng Zou; Yi Zhou 017 (NeurIPS, 2021)

Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity. - Ziyi Chen 002; Yi Zhou 017; Rongrong Chen (CoRR, 2021)

Finite-Sample Analysis for Two Time-scale Non-linear TDC with General Smooth Function Approximation. - Yue Wang 068; Shaofeng Zou; Yi Zhou 017 (CoRR, 2021)

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. - Ziyi Chen 002; Yi Zhou 017; Rongrong Chen; Shaofeng Zou (CoRR, 2021)

Assisted Learning for Organizations with Limited Data. - Cheng Chen; Jiaying Zhou; Jie Ding 002; Yi Zhou 017 (CoRR, 2021)

Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent. - Ziyi Chen 002; Qunwei Li; Yi Zhou 017 (CoRR, 2021)

MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. - Qunwei Li; Bhavya Kailkhura; Rushil Anirudh; Jize Zhang; Yi Zhou 017; Yingbin Liang; Thomas Yong-Jin Han; Pramod K. Varshney (SIAM J. Math. Data Sci., 2021)

Understanding Estimation and Generalization Error of Generative Adversarial Networks. - Kaiyi Ji; Yi Zhou 017; Yingbin Liang (IEEE Trans. Inf. Theory, 2021)

Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game. - Ziyi Chen 002; Shaocong Ma; Yi Zhou 017 (ICLR, 2022)

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. - Ziyi Chen 002; Yi Zhou 017; Rong-Rong Chen; Shaofeng Zou (ICML, 2022)

Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. - Ziyi Chen 002; Shaocong Ma; Yi Zhou 017 (ISIT, 2022)

Data-Driven Robust Multi-Agent Reinforcement Learning. - Yudan Wang; Yue Wang 068; Yi Zhou 017; Alvaro Velasquez; Shaofeng Zou (MLSP, 2022)

Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach. - Ziyi Chen 002; Shaocong Ma; Yi Zhou 017 (NeurIPS, 2022)

Data sampling affects the complexity of online SGD over dependent data. - Shaocong Ma; Ziyi Chen 002; Yi Zhou 017; Kaiyi Ji; Yingbin Liang (UAI, 2022)

A new one-point residual-feedback oracle for black-box learning and control. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Michael M. Zavlanos (Autom., 2022)

A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. - Ziyi Chen 002; Bhavya Kailkhura; Yi Zhou 017 (CoRR, 2022)

Understanding generalization error of SGD in nonconvex optimization. - Yi Zhou 017; Yingbin Liang; Huishuai Zhang (Mach. Learn., 2022)

Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities. - Ziyi Chen 002; Yi Zhou 017; Rong-Rong Chen (Trans. Mach. Learn. Res., 2022)

Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication. - Joohyun Cho; Mingxi Liu; Yi Zhou 017; Rong-Rong Chen (ACSSC, 2023)

Exploring Gradient Oscillation in Deep Neural Network Training. - Chedi Morchdi; Yi Zhou 017; Jie Ding 002; Bei Wang 001 (Allerton, 2023)

Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback. - Ziwei Guan; Yi Zhou 017; Yingbin Liang (COLT, 2023)

Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. - Ziyi Chen 002; Yi Zhou 017; Yingbin Liang; Zhaosong Lu (ICML, 2023)

Assisted Unsupervised Domain Adaptation. - Cheng Chen; Jiawei Zhang 007; Jie Ding 002; Yi Zhou 017 (ISIT, 2023)

Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. - Youjia Zhou; Yi Zhou 017; Jie Ding 002; Bei Wang 001 (TAG-ML, 2023)

Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty. - Shaocong Ma; Ziyi Chen 002; Shaofeng Zou; Yi Zhou 017 (J. Mach. Learn. Res., 2023)

Assisted Learning for Organizations with Limited Imbalanced Data. - Cheng Chen; Jiaying Zhou; Jie Ding 002; Yi Zhou 017 (Trans. Mach. Learn. Res., 2023)

Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. - Qi Zhang 069; Yi Zhou 017; Ashley Prater-Bennette; Lixin Shen; Shaofeng Zou (AAAI, 2024)

A Resource-efficient Task Scheduling System using Reinforcement Learning : Invited Paper. - Chedi Morchdi; Cheng-Hsiang Chiu; Yi Zhou 017; Tsung-Wei Huang (ASPDAC, 2024)

Reinforcement Learning-Generated Topological Order for Dynamic Task Graph Scheduling. - Cheng-Hsiang Chiu; Chedi Morchdi; Yi Zhou 017; Boyang Zhang; Che Chang; Tsung-Wei Huang (HPEC, 2024)

On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning. - Ziyi Chen 002; Yi Zhou 017; Heng Huang (ICLR, 2024)

On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback. - Ziwei Guan; Yi Zhou 017; Yingbin Liang (ICLR, 2024)

Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. - Yudan Wang; Yue Wang 068; Yi Zhou 017; Shaofeng Zou (ICML, 2024)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. - Shaocong Ma; James Diffenderfer; Bhavya Kailkhura; Yi Zhou 017 (CoRR, 2024)

Finite-time error bounds for Greedy-GQ. - Yue Wang 068; Yi Zhou 017; Shaofeng Zou (Mach. Learn., 2024)

Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. - Yan Zhang 043; Yi Zhou 017; Kaiyi Ji; Yi Shen 011; Michael M. Zavlanos (IEEE Trans. Autom. Control., 2024)

Revisiting Large-Scale Non-convex Distributionally Robust Optimization. - Qi Zhang 069; Yi Zhou 017; Simon Khan; Ashley Prater-Bennette; Lixin Shen; Shaofeng Zou (ICLR, 2025)

Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. - Qi Zhang 069; Yi Zhou 017; Shaofeng Zou (Trans. Mach. Learn. Res., 2025)

Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality. - Shaocong Ma; Ziyi Chen 002; Yi Zhou 017; Heng Huang (Trans. Mach. Learn. Res., 2025)

Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization. - Yufeng Yang; Erin E. Tripp; Yifan Sun; Shaofeng Zou; Yi Zhou 017 (Trans. Mach. Learn. Res., 2025)

Nested Stochastic Gradient Descent for (Generalized) Sinkhorn Distance-Regularized Distributionally Robust Optimization. - Yufeng Yang; Yi Zhou 017; Zhaosong Lu (CoRR, 2025)

Deep learning of PDE correction and mesh adaption without automatic differentiation. - Shaocong Ma; James Diffenderfer; Bhavya Kailkhura; Yi Zhou 017 (Mach. Learn., 2025)