ML Anthology

Yin, Wotao

53 publications

NeurIPS 2025 ComPO: Preference Alignment via Comparison Oracles Peter Chen, Xi Chen, Wotao Yin, Tianyi Lin
ICML 2025 Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin
NeurIPS 2025 Subsampled Ensemble Can Improve Generalization Tail Exponentially Huajie Qian, Donghao Ying, Henry Lam, Wotao Yin
ICML 2024 Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares Liangzu Peng, Wotao Yin
TMLR 2024 DIG-MILP: A Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee Haoyu Peter Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin
ICML 2024 Efficient Algorithms for Sum-of-Minimum Optimization Lisang Ding, Ziang Chen, Xinshang Wang, Wotao Yin
TMLR 2024 Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen
NeurIPS 2024 Rethinking the Capacity of Graph Neural Networks for Branching Strategy Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang, Wotao Yin
ICML 2024 Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
AISTATS 2023 Alternating Projected SGD for Equality-Constrained Bilevel Optimization Quan Xiao, Han Shen, Wotao Yin, Tianyi Chen
TMLR 2023 Attentional-Biased Stochastic Gradient Descent Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
ICML 2023 DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm Lisang Ding, Kexin Jin, Bicheng Ying, Kun Yuan, Wotao Yin
AISTATS 2023 HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent Ziang Chen, Jianfeng Lu, Huajie Qian, Xinshang Wang, Wotao Yin
ICLR 2023 On Representing Linear Programs by Graph Neural Networks Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin
ICLR 2023 On Representing Mixed-Integer Linear Programs by Graph Neural Networks Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin
AAAI 2023 Safeguarded Learned Convex Optimization Howard Heaton, Xiaohan Chen, Zhangyang Wang, Wotao Yin
ICML 2023 Towards Constituting Mathematical Structures for Learning to Optimize Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, Hanqin Cai
AISTATS 2022 A Single-Timescale Method for Stochastic Bilevel Optimization Tianyi Chen, Yuejiao Sun, Quan Xiao, Wotao Yin
NeurIPS 2022 Communication-Efficient Topologies for Decentralized Learning with $o(1)$ Consensus Rate Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi, Ming Yan, Wotao Yin, Kun Yuan
NeurIPS 2022 FiLM: Frequency Improved Legendre Memory Model for Long-Term Time Series Forecasting Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin
AAAI 2022 JFB: Jacobian-Free Backpropagation for Implicit Networks Samy Wu Fung, Howard Heaton, Qiuwei Li, Daniel McKenzie, Stanley J. Osher, Wotao Yin
JMLR 2022 Learning to Optimize: A Primer and a Benchmark Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin
NeurIPS 2022 Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan
AISTATS 2021 CADA: Communication-Adaptive Distributed Adam Tianyi Chen, Ziye Guo, Yuejiao Sun, Wotao Yin
ICML 2021 A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization Hanqin Cai, Yuchen Lou, Daniel Mckenzie, Wotao Yin
ICML 2021 Accelerating Gossip SGD with Periodic Global Averaging Yiming Chen, Kun Yuan, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin
NeurIPS 2021 An Improved Analysis and Rates for Variance Reduction Under Without-Replacement Sampling Orders Xinmeng Huang, Kun Yuan, Xianghui Mao, Wotao Yin
IJCAI 2021 AutoBandit: A Meta Bandit Online Learning System Miao Xie, Wotao Yin, Huan Xu
NeurIPS 2021 Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems Tianyi Chen, Yuejiao Sun, Wotao Yin
ICCV 2021 DecentLaM: Decentralized Momentum SGD for Large-Batch Deep Training Kun Yuan, Yiming Chen, Xinmeng Huang, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin
NeurIPS 2021 Exponential Graph Is Provably Efficient for Decentralized Deep Training Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin
NeurIPS 2021 Hyperparameter Tuning Is All You Need for LISTA Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin
NeurIPS 2021 Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection HanQin Cai, Jialin Liu, Wotao Yin
ICLR 2021 Learning a Minimax Optimizer: A Pilot Study Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang
ICML 2021 Provably Correct Optimization and Exploration with Non-Linear Policies Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang
NeurIPS 2020 An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods Yanli Liu, Kaiqing Zhang, Tamer Basar, Wotao Yin
NeurIPS 2020 An Improved Analysis of Stochastic Gradient Descent with Momentum Yanli Liu, Yuan Gao, Wotao Yin
AISTATS 2020 AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity Yibo Zeng, Fei Feng, Wotao Yin
NeurIPS 2020 Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning Fei Feng, Ruosong Wang, Wotao Yin, Simon S Du, Lin Yang
ICLR 2019 A2BCD: Asynchronous Acceleration with Optimal Complexity Robert Hannah, Fei Feng, Wotao Yin
ICLR 2019 ALISTA: Analytic Weights Are as Good as Learned Weights in LISTA Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin
ICML 2019 Acceleration of SVRG and Katyusha X by Inexact Preconditioning Yanli Liu, Fei Feng, Wotao Yin
ICML 2019 Plug-and-Play Methods Provably Converge with Properly Trained Denoisers Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin
JMLR 2019 Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
NeurIPS 2018 Breaking the Span Assumption Yields Fast Finite-Sum Minimization Robert Hannah, Yanli Liu, Daniel O'Connor, Wotao Yin
NeurIPS 2018 LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen, Georgios Giannakis, Tao Sun, Wotao Yin
NeurIPS 2018 On Markov Chain Gradient Descent Tao Sun, Yuejiao Sun, Wotao Yin
NeurIPS 2018 Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin
NeurIPS 2017 Asynchronous Coordinate Descent Under More Realistic Assumptions Tao Sun, Robert Hannah, Wotao Yin
NeurIPS 2017 Straggler Mitigation in Distributed Optimization Through Data Encoding Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
JMLR 2010 A Fast Hybrid Algorithm for Large-Scale L1-Regularized Logistic Regression Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda
CVPR 2008 An Efficient Algorithm for Compressed MR Imaging Using Total Variation and Wavelets Shiqian Ma, Wotao Yin, Yin Zhang, Amit Chakraborty
CVPR 2005 Illumination Normalization for Face Recognition and Uneven Background Correction Using Total Variation Based Image Models Terrence Chen, Wotao Yin, Xiang Sean Zhou, Dorin Comaniciu, Thomas S. Huang