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ORCIDYi Zhou 0017
Person information
- affiliation: Texas A&M University, College Station, TX, USA
- affiliation (until 2024): University of Utah, Department of Electrical and Computer Engineering, Salt Lake City, UT, USA
Other persons with the same name
- Yi Zhou — disambiguation page
- Yi Zhou 0001
![[0000-0001-5420-8016]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-5420-8016")
— Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing, China - Yi Zhou 0002 — Singapore Polytechnic,School of Electrical and Electronic Engineering, Singapore (and 1 more)
- Yi Zhou 0003 — Shanghai Jiao Tong University, Computer Science Department, China
- Yi Zhou 0004 — Henan University, School of Computer and Information Engineering, Kaifeng, China (and 1 more)
- Yi Zhou 0005 — Sun Yat-sen University, Zhongshan School of Medicine, Guangzhou, China
- Yi Zhou 0006 — Monash University, Department of Management, Caulfield East, VIC, Australia (and 1 more)
- Yi Zhou 0007 — Southeast University, Nanjing, Jiangsu, China (and 2 more)
- Yi Zhou 0008 — Fudan University, Shanghai Engineering Research Center of Ultra Precision Optical Manufacturing, Shanghai, China
- Yi Zhou 0009 — Columbus State University, GA, USA (and 1 more)
- Yi Zhou 0010 — Hunan University, China (and 1 more)
- Yi Zhou 0011 — Dalian Maritime University, College of Information Science and Technology, China (and 1 more)
- Yi Zhou 0012 — Southwest Jiaotong University, Provincial Key Laboratory of Information Coding and Transmission, Chengdu, China (and 1 more)
- Yi Zhou 0013 — University of Western Sydney, School of Computing, Engineering and Mathematics, Australia
- Yi Zhou 0014 — Chongqing University of Posts and Telecommunications, School of Communication and Information Engineering, China (and 2 more)
- Yi Zhou 0015 — IBM Research - Almaden, San Jose, CA, USA
- Yi Zhou 0016 — University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu, China
- Yi Zhou 0018 — Bytedance AI Lab, China (and 1 more)
- Yi Zhou 0019 — University of Liverpool, UK
- Yi Zhou 0020 — National University of Singapore, Department of Electrical, and Computer Engineering, Singapore
- Yi Zhou 0022 — Wuhan University of Science and Technology, School of Information Science and Engineering, Engineering Research Center of Metallurgical Automation and Measurement Technology, China
- Yi Zhou 0023 — Adobe, USA (and 2 more)
- Yi Zhou 0024 — Soochow University, School of Electronics and Information Engineering, China
- Yi Zhou 0025 — Carnegie Mellon University, Pittsburgh, PA, USA
- Yi Zhou 0026 — University ofElectronic Science and Technology of China, School of Automation Engineering, China
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Journal Articles
- 2025
[j17]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
Deep learning of PDE correction and mesh adaption without automatic differentiation. Mach. Learn. 114(3): 61 (2025)- [j16]Qi Zhang, Yi Zhou, Shaofeng Zou:
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. Trans. Mach. Learn. Res. 2025 (2025) - [j15]Shaocong Ma, Ziyi Chen, Yi Zhou, Heng Huang:
Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality. Trans. Mach. Learn. Res. 2025 (2025) - [j14]Yufeng Yang, Erin E. Tripp, Yifan Sun, Shaofeng Zou, Yi Zhou:
Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization. Trans. Mach. Learn. Res. 2025 (2025) - 2024
- [j13]Yue Wang, Yi Zhou, Shaofeng Zou:
Finite-time error bounds for Greedy-GQ. Mach. Learn. 113(9): 5981-6018 (2024) - [j12]Yan Zhang, Yi Zhou, Kaiyi Ji, Yi Shen, Michael M. Zavlanos:
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. IEEE Trans. Autom. Control. 69(9): 6309-6316 (2024) - 2023
- [j11]Shaocong Ma, Ziyi Chen, Shaofeng Zou, Yi Zhou:
Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty. J. Mach. Learn. Res. 24: 371:1-371:40 (2023) - [j10]Ziyi Chen, Zhengyang Hu, Qunwei Li, Zhe Wang, Yi Zhou:
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization. Trans. Mach. Learn. Res. 2023 (2023) - [j9]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Imbalanced Data. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j8]Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos:
A new one-point residual-feedback oracle for black-box learning and control. Autom. 136: 110006 (2022) - [j7]Yi Zhou, Yingbin Liang, Huishuai Zhang:
Understanding generalization error of SGD in nonconvex optimization. Mach. Learn. 111(1): 345-375 (2022) - [j6]Ziyi Chen, Yi Zhou, Rong-Rong Chen:
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities. Trans. Mach. Learn. Res. 2022 (2022) - 2021
- [j5]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Jize Zhang, Yi Zhou, Yingbin Liang, Thomas Yong-Jin Han, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data. SIAM J. Math. Data Sci. 3(4): 1197-1222 (2021) - [j4]Kaiyi Ji, Yi Zhou, Yingbin Liang:
Understanding Estimation and Generalization Error of Generative Adversarial Networks. IEEE Trans. Inf. Theory 67(5): 3114-3129 (2021) - 2019
- [j3]Yi Zhou, Yingbin Liang, Lixin Shen:
A simple convergence analysis of Bregman proximal gradient algorithm. Comput. Optim. Appl. 73(3): 903-912 (2019) - [j2]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A note on inexact gradient and Hessian conditions for cubic regularized Newton's method. Oper. Res. Lett. 47(2): 146-149 (2019) - 2018
- [j1]Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing:
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. 19: 19:1-19:32 (2018)
Conference and Workshop Papers
- 2025
- [c55]Qi Zhang, Yi Zhou, Simon Khan, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou:
Revisiting Large-Scale Non-convex Distributionally Robust Optimization. ICLR 2025 - 2024
- [c54]Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou:
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. AAAI 2024: 8217-8225 - [c53]Chedi Morchdi, Cheng-Hsiang Chiu, Yi Zhou, Tsung-Wei Huang:
A Resource-efficient Task Scheduling System using Reinforcement Learning : Invited Paper. ASPDAC 2024: 89-95 - [c52]Cheng-Hsiang Chiu, Chedi Morchdi, Yi Zhou, Boyang Zhang, Che Chang, Tsung-Wei Huang:
Reinforcement Learning-Generated Topological Order for Dynamic Task Graph Scheduling. HPEC 2024: 1-7 - [c51]Ziyi Chen, Yi Zhou, Heng Huang:
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning. ICLR 2024 - [c50]Ziwei Guan, Yi Zhou, Yingbin Liang:
On the Hardness of Online Nonconvex Optimization with Single Oracle Feedback. ICLR 2024 - [c49]Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou:
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. ICML 2024 - 2023
- [c48]Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen:
Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication. ACSSC 2023: 1394-1398 - [c47]Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang:
Exploring Gradient Oscillation in Deep Neural Network Training. Allerton 2023: 1-7 - [c46]Ziwei Guan, Yi Zhou, Yingbin Liang:
Online Nonconvex Optimization with Limited Instantaneous Oracle Feedback. COLT 2023: 3328-3355 - [c45]Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu:
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. ICML 2023: 5396-5427 - [c44]Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou:
Assisted Unsupervised Domain Adaptation. ISIT 2023: 2482-2487 - [c43]Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu:
Edge-cloud Collaborative Learning with Federated and Centralized Features. SIGIR 2023: 1949-1953 - [c42]Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang:
Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. TAG-ML 2023: 134-145 - 2022
- [c41]Ziyi Chen, Shaocong Ma, Yi Zhou:
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game. ICLR 2022 - [c40]Ziyi Chen, Yi Zhou, Rong-Rong Chen, Shaofeng Zou:
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. ICML 2022: 3794-3834 - [c39]Ziyi Chen, Shaocong Ma, Yi Zhou:
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. ISIT 2022: 672-677 - [c38]Yudan Wang, Yue Wang, Yi Zhou, Alvaro Velasquez, Shaofeng Zou:
Data-Driven Robust Multi-Agent Reinforcement Learning. MLSP 2022: 1-6 - [c37]Ziyi Chen, Shaocong Ma, Yi Zhou:
Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach. NeurIPS 2022 - [c36]Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Data sampling affects the complexity of online SGD over dependent data. UAI 2022: 1296-1305 - 2021
- [c35]Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen:
Communication-Free Two-Stage Multi-Agent DDPG under Partial States and Observations. ACSCC 2021: 459-463 - [c34]Ziyi Chen, Yi Zhou, Rongrong Chen:
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexity. ACSCC 2021: 504-508 - [c33]Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang:
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. ICLR 2021 - [c32]Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou:
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. ICLR 2021 - [c31]Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou:
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. MASS 2021: 173-179 - [c30]Yue Wang, Shaofeng Zou, Yi Zhou:
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation. NeurIPS 2021: 9747-9758 - 2020
- [c29]Cheng Chen, Junjie Yang, Yi Zhou:
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. IEEE BigData 2020: 141-146 - [c28]Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura:
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. IEEE BigData 2020: 5017-5026 - [c27]Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh:
Supervised Encoding for Discrete Representation Learning. ICASSP 2020: 3447-3451 - [c26]Chris Cannella, Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh:
Perception-Distortion Trade-Off with Restricted Boltzmann Machines. ICASSP 2020: 4022-4026 - [c25]Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang:
Reanalysis of Variance Reduced Temporal Difference Learning. ICLR 2020 - [c24]Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang:
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. ICML 2020: 4762-4772 - [c23]Shaocong Ma, Yi Zhou:
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. ICML 2020: 6565-6574 - [c22]Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh:
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. IJCAI 2020: 1445-1451 - [c21]Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer:
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. NeurIPS 2020 - [c20]Shaocong Ma, Yi Zhou, Shaofeng Zou:
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. NeurIPS 2020 - 2019
- [c19]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. AISTATS 2019: 2731-2740 - [c18]Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang:
Distributed SGD Generalizes Well Under Asynchrony. Allerton 2019: 863-870 - [c17]Yi Feng, Yi Zhou, Vahid Tarokh:
Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. IEEE BigData 2019: 2240-2246 - [c16]Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. ICLR (Poster) 2019 - [c15]Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. ICLR (Poster) 2019 - [c14]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. ICML 2019: 3100-3109 - [c13]Yi Zhou, Yi Feng, Vahid Tarokh, Vadas Gintautas, Jessee McClelland, Denis Garagic:
Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation. MLSP 2019: 1-6 - [c12]Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost and Momentum: Faster Variance Reduction Algorithms. NeurIPS 2019: 2403-2413 - [c11]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. UAI 2019: 313-322 - 2018
- [c10]Yi Zhou, Yingbin Liang:
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. ICLR (Poster) 2018 - [c9]Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. NeurIPS 2018: 3764-3773 - 2017
- [c8]Yi Zhou, Yingbin Liang:
Demixing sparse signals via convex optimization. ICASSP 2017: 4202-4206 - [c7]Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. ICML 2017: 2111-2119 - [c6]Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing:
Learning Latent Space Models with Angular Constraints. ICML 2017: 3799-3810 - 2016
- [c5]Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing:
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS 2016: 713-722 - [c4]Yi Zhou, Huishuai Zhang, Yingbin Liang:
On Compressive orthonormal Sensing. Allerton 2016: 299-305 - [c3]Yi Zhou, Huishuai Zhang, Yingbin Liang:
Geometrical properties and accelerated gradient solvers of non-convex phase retrieval. Allerton 2016: 331-335 - [c2]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI 2016 - 2015
- [c1]Huishuai Zhang, Yi Zhou, Yingbin Liang:
Analysis of Robust PCA via Local Incoherence. NIPS 2015: 1819-1827
Informal and Other Publications
- 2025
- [i46]Yufeng Yang, Yi Zhou, Zhaosong Lu:
Nested Stochastic Gradient Descent for (Generalized) Sinkhorn Distance-Regularized Distributionally Robust Optimization. CoRR abs/2503.22923 (2025) - [i45]Shaocong Ma, Ziyi Chen, Yi Zhou, Heng Huang:
Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality. CoRR abs/2508.17448 (2025) - 2024
- [i44]Qi Zhang, Yi Zhou, Ashley Prater-Bennette, Lixin Shen, Shaofeng Zou:
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization. CoRR abs/2404.01200 (2024) - [i43]Qi Zhang, Yi Zhou, Shaofeng Zou:
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. CoRR abs/2404.01436 (2024) - [i42]Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou:
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver. CoRR abs/2404.11766 (2024) - [i41]Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou:
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation. CoRR abs/2406.01762 (2024) - 2023
- [i40]Zexi Li, Qunwei Li, Yi Zhou, Wenliang Zhong, Guannan Zhang, Chao Wu:
Edge-cloud Collaborative Learning with Federated and Centralized Features. CoRR abs/2304.05871 (2023) - 2022
- [i39]Ziyi Chen, Bhavya Kailkhura, Yi Zhou:
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization. CoRR abs/2203.16615 (2022) - [i38]Shaocong Ma, Ziyi Chen, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Data Sampling Affects the Complexity of Online SGD over Dependent Data. CoRR abs/2204.00006 (2022) - [i37]Yue Wang, Yi Zhou, Shaofeng Zou:
Finite-Time Error Bounds for Greedy-GQ. CoRR abs/2209.02555 (2022) - 2021
- [i36]Ziyi Chen, Yi Zhou, Tengyu Xu, Yingbin Liang:
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. CoRR abs/2102.04653 (2021) - [i35]Ziyi Chen, Yi Zhou, Rongrong Chen:
Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity. CoRR abs/2103.13147 (2021) - [i34]Cheng Chen, Bhavya Kailkhura, Ryan A. Goldhahn, Yi Zhou:
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing. CoRR abs/2103.16031 (2021) - [i33]Shaocong Ma, Ziyi Chen, Yi Zhou, Shaofeng Zou:
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity. CoRR abs/2103.16377 (2021) - [i32]Yue Wang, Shaofeng Zou, Yi Zhou:
Finite-Sample Analysis for Two Time-scale Non-linear TDC with General Smooth Function Approximation. CoRR abs/2104.02836 (2021) - [i31]Ziyi Chen, Yi Zhou, Rongrong Chen, Shaofeng Zou:
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis. CoRR abs/2109.03699 (2021) - [i30]Cheng Chen, Jiaying Zhou, Jie Ding, Yi Zhou:
Assisted Learning for Organizations with Limited Data. CoRR abs/2109.09307 (2021) - [i29]Ziyi Chen, Qunwei Li, Yi Zhou:
Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent. CoRR abs/2110.07098 (2021) - [i28]Ziyi Chen, Shaocong Ma, Yi Zhou:
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning. CoRR abs/2112.11663 (2021) - 2020
- [i27]Tengyu Xu, Zhe Wang, Yi Zhou, Yingbin Liang:
Reanalysis of Variance Reduced Temporal Difference Learning. CoRR abs/2001.01898 (2020) - [i26]Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh:
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. CoRR abs/2002.11582 (2020) - [i25]Ziyi Chen, Yi Zhou:
Momentum with Variance Reduction for Nonconvex Composition Optimization. CoRR abs/2005.07755 (2020) - [i24]Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos:
Improving the Convergence Rate of One-Point Zeroth-Order Optimization using Residual Feedback. CoRR abs/2006.10820 (2020) - [i23]Shaocong Ma, Yi Zhou:
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. CoRR abs/2007.03509 (2020) - [i22]Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura:
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. CoRR abs/2009.10748 (2020) - [i21]Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos:
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback. CoRR abs/2010.07378 (2020) - [i20]Shaocong Ma, Yi Zhou, Shaofeng Zou:
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. CoRR abs/2010.13272 (2020) - [i19]Cheng Chen, Junjie Yang, Yi Zhou:
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. CoRR abs/2011.06702 (2020) - 2019
- [i18]Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh:
SGD Converges to Global Minimum in Deep Learning via Star-convex Path. CoRR abs/1901.00451 (2019) - [i17]Jayanth Regatti, Gaurav Tendolkar, Yi Zhou, Abhishek Gupta, Yingbin Liang:
Distributed SGD Generalizes Well Under Asynchrony. CoRR abs/1909.13391 (2019) - [i16]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation. CoRR abs/1910.09670 (2019) - [i15]Cat P. Le, Yi Zhou, Jie Ding, Vahid Tarokh:
Supervised Encoding for Discrete Representation Learning. CoRR abs/1910.11067 (2019) - [i14]Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. CoRR abs/1910.12166 (2019) - 2018
- [i13]Yi Zhou, Yingbin Liang, Huishuai Zhang:
Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization. CoRR abs/1802.06903 (2018) - [i12]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. CoRR abs/1802.07372 (2018) - [i11]Tengyu Xu, Yi Zhou, Kaiyi Ji, Yingbin Liang:
Convergence of SGD in Learning ReLU Models with Separable Data. CoRR abs/1806.04339 (2018) - [i10]Yi Zhou, Zhe Wang, Yingbin Liang:
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property. CoRR abs/1808.07382 (2018) - [i9]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
A Note on Inexact Condition for Cubic Regularized Newton's Method. CoRR abs/1808.07384 (2018) - [i8]Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. CoRR abs/1810.03264 (2018) - [i7]Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan:
Cubic Regularization with Momentum for Nonconvex Optimization. CoRR abs/1810.03763 (2018) - [i6]Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh:
SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization. CoRR abs/1810.10690 (2018) - [i5]Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
MR-GAN: Manifold Regularized Generative Adversarial Networks. CoRR abs/1811.10427 (2018) - 2017
- [i4]Qunwei Li, Yi Zhou, Yingbin Liang, Pramod K. Varshney:
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization. CoRR abs/1705.04925 (2017) - [i3]Yi Zhou, Yingbin Liang:
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks. CoRR abs/1710.06910 (2017) - [i2]Yi Zhou, Yingbin Liang:
Critical Points of Neural Networks: Analytical Forms and Landscape Properties. CoRR abs/1710.11205 (2017) - 2015
- [i1]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Distributed Machine Learning via Sufficient Factor Broadcasting. CoRR abs/1511.08486 (2015)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-10-30 00:52 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
