Yi Zhou

According to our database, Yi Zhou authored at least 71 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Assisted Learning for Organizations with Limited Imbalanced Data.

Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou

Trans. Mach. Learn. Res., 2023

Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training.

Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang

Proceedings of the Topological, 2023

Assisted Unsupervised Domain Adaptation.

Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou

Proceedings of the IEEE International Symposium on Information Theory, 2023

Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization.

Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu

Proceedings of the International Conference on Machine Learning, 2023

Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback.

Ziwei Guan, Yi Zhou, Yingbin Liang

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Exploring Gradient Oscillation in Deep Neural Network Training.

Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang

Proceedings of the 59th Annual Allerton Conference on Communication, 2023

Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication.

Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen

Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

2022

Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities.

Ziyi Chen, Yi Zhou, Rong-Rong Chen

Trans. Mach. Learn. Res., 2022

Understanding generalization error of SGD in nonconvex optimization.

Yi Zhou, Yingbin Liang, Huishuai Zhang

Mach. Learn., 2022

Finite-Time Error Bounds for Greedy-GQ.

Yue Wang, Yi Zhou, Shaofeng Zou

CoRR, 2022

A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization.

Ziyi Chen, Bhavya Kailkhura, Yi Zhou

CoRR, 2022

A new one-point residual-feedback oracle for black-box learning and control.

Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos

Autom., 2022

Data sampling affects the complexity of online SGD over dependent data.

Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach.

Ziyi Chen, Shaocong Ma, Yi Zhou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data-Driven Robust Multi-Agent Reinforcement Learning.

Yudan Wang, Yue Wang, Yi Zhou, Alvaro Velasquez, Shaofeng Zou

Proceedings of the 32nd IEEE International Workshop on Machine Learning for Signal Processing, 2022

Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning.

Ziyi Chen, Shaocong Ma, Yi Zhou

Proceedings of the IEEE International Symposium on Information Theory, 2022

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis.

Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou

Proceedings of the International Conference on Machine Learning, 2022

Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game.

Ziyi Chen, Shaocong Ma, Yi Zhou

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Understanding Estimation and Generalization Error of Generative Adversarial Networks.

Kaiyi Ji, Yi Zhou, Yingbin Liang

IEEE Trans. Inf. Theory, 2021

MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data.

Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney

SIAM J. Math. Data Sci., 2021

Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent.

Ziyi Chen, Qunwei Li, Yi Zhou

CoRR, 2021

Assisted Learning for Organizations with Limited Data.

Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou

CoRR, 2021

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis.

Ziyi Chen, Yi Zhou, Rongrong Chen, Shaofeng Zou

CoRR, 2021

Finite-Sample Analysis for Two Time-scale Non-linear TDC with General Smooth Function Approximation.

Yue Wang, Shaofeng Zou, Yi Zhou

CoRR, 2021

Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity.

Ziyi Chen, Yi Zhou, Rongrong Chen

CoRR, 2021

Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation.

Yue Wang, Shaofeng Zou, Yi Zhou

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing.

Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou

Proceedings of the IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, 2021

Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity.

Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou

Proceedings of the 9th International Conference on Learning Representations, 2021

Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry.

Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang

Proceedings of the 9th International Conference on Learning Representations, 2021

Communication-Free Two-Stage Multi-Agent DDPG under Partial States and Observations.

Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen

Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexity.

Ziyi Chen, Yi Zhou, Rongrong Chen

Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020

Boosting One-Point Derivative-Free Online Optimization via Residual Feedback.

Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos

CoRR, 2020

Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback.

Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos

CoRR, 2020

Momentum with Variance Reduction for Nonconvex Composition Optimization.

Ziyi Chen, Yi Zhou

CoRR, 2020

Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis.

Shaocong Ma, Yi Zhou, Shaofeng Zou

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization.

Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle.

Shaocong Ma, Yi Zhou

Proceedings of the 37th International Conference on Machine Learning, 2020

History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms.

Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang

Proceedings of the 37th International Conference on Machine Learning, 2020

Reanalysis of Variance Reduced Temporal Difference Learning.

Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang

Proceedings of the 8th International Conference on Learning Representations, 2020

Supervised Encoding for Discrete Representation Learning.

Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Perception-Distortion Trade-Off with Restricted Boltzmann Machines.

Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study.

Cheng Chen, Junjie Yang, Yi Zhou

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling.

Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

A note on inexact gradient and Hessian conditions for cubic regularized Newton's method.

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

Oper. Res. Lett., 2019

Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation.

Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang

CoRR, 2019

A simple convergence analysis of Bregman proximal gradient algorithm.

Yi Zhou, Yingbin Liang, Lixin Shen

Comput. Optim. Appl., 2019

Cubic Regularization with Momentum for Nonconvex Optimization.

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

SpiderBoost and Momentum: Faster Variance Reduction Algorithms.

Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation.

Yi Zhou, Yi Feng, Vahid Tarokh, Vadas Gintautas, Jessee McClelland, Denis Garagic

Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization.

Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang

Proceedings of the 36th International Conference on Machine Learning, 2019

SGD Converges to Global Minimum in Deep Learning via Star-convex Path.

Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh

Proceedings of the 7th International Conference on Learning Representations, 2019

Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing.

Yi Feng, Yi Zhou, Vahid Tarokh

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Distributed SGD Generalizes Well Under Asynchrony.

Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang

Proceedings of the 57th Annual Allerton Conference on Communication, 2019

Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization.

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters.

Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing

J. Mach. Learn. Res., 2018

MR-GAN: Manifold Regularized Generative Adversarial Networks.

Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney

CoRR, 2018

SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization.

Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh

CoRR, 2018

A Note on Inexact Condition for Cubic Regularized Newton's Method.

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

CoRR, 2018

Convergence of SGD in Learning ReLU Models with Separable Data.

Tengyu Xu, Yi Zhou, Kaiyi Ji, Yingbin Liang

CoRR, 2018

Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization.

Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan

CoRR, 2018

Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization.

Yi Zhou, Yingbin Liang, Huishuai Zhang

CoRR, 2018

Convergence of Cubic Regularization for Nonconvex Optimization under KL Property.

Yi Zhou, Zhe Wang, Yingbin Liang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties.

Yi Zhou, Yingbin Liang

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Critical Points of Neural Networks: Analytical Forms and Landscape Properties.

Yi Zhou, Yingbin Liang

CoRR, 2017

Characterization of Gradient Dominance and Regularity Conditions for Neural Networks.

Yi Zhou, Yingbin Liang

CoRR, 2017

Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization.

Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney

Proceedings of the 34th International Conference on Machine Learning, 2017

Demixing sparse signals via convex optimization.

Yi Zhou, Yingbin Liang

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

Geometrical properties and accelerated gradient solvers of non-convex phase retrieval.

Yi Zhou, Huishuai Zhang, Yingbin Liang

Proceedings of the 54th Annual Allerton Conference on Communication, 2016

On Compressive orthonormal Sensing.

Yi Zhou, Huishuai Zhang, Yingbin Liang

Proceedings of the 54th Annual Allerton Conference on Communication, 2016

On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System.

Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Analysis of Robust PCA via Local Incoherence.

Huishuai Zhang, Yi Zhou, Yingbin Liang

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015