Xiao, Lin

43 publications

NeurIPS 2025 Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RL Tong Yang, Bo Dai, Lin Xiao, Yuejie Chi

ICML 2025 Incentivize Without Bonus: Provably Efficient Model-Based Online Multi-Agent RL for Markov Games Tong Yang, Bo Dai, Lin Xiao, Yuejie Chi

ICML 2025 PARQ: Piecewise-Affine Regularized Quantization Lisa Jin, Jianhao Ma, Zechun Liu, Andrey Gromov, Aaron Defazio, Lin Xiao

NeurIPS 2025 ParetoQ: Improving Scaling Laws in Extremely Low-Bit LLM Quantization Zechun Liu, Changsheng Zhao, Hanxian Huang, Sijia Chen, Jing Zhang, Jiawei Zhao, Scott Roy, Lisa Jin, Yunyang Xiong, Yangyang Shi, Lin Xiao, Yuandong Tian, Bilge Soran, Raghuraman Krishnamoorthi, Tijmen Blankevoort, Vikas Chandra

ICMLW 2024 Dual Approximation Policy Optimization Zhihan Xiong, Maryam Fazel, Lin Xiao

ICLR 2023 Faster Last-Iterate Convergence of Policy Optimization in Zero-Sum Markov Games Shicong Cen, Yuejie Chi, Simon Shaolei Du, Lin Xiao

AAAI 2023 Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification Pengyu Xu, Lin Xiao, Bing Liu, Sijin Lu, Liping Jing, Jian Yu

ICLR 2023 Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies Rui Yuan, Simon Shaolei Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao

NeurIPS 2022 BiT: Robustly Binarized Multi-Distilled Transformer Zechun Liu, Barlas Oguz, Aasish Pappu, Lin Xiao, Scott Yih, Meng Li, Raghuraman Krishnamoorthi, Yashar Mehdad

TMLR 2022 FedShuffle: Recipes for Better Use of Local Work in Federated Learning Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat

ICML 2022 Federated Learning with Partial Model Personalization Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, Lin Xiao

JMLR 2022 On the Convergence Rates of Policy Gradient Methods Lin Xiao

AAAI 2021 Does Head Label Help for Long-Tailed Multi-Label Text Classification Lin Xiao, Xiangliang Zhang, Liping Jing, Chi Huang, Mingyang Song

JMLR 2021 From Low Probability to High Confidence in Stochastic Convex Optimization Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang

ACML 2021 Hybrid Summarization with Semantic Weighting Reward and Latent Structure Detector Mingyang Song, Liping Jing, Yi Feng, Zhiwei Sun, Lin Xiao

AAAI 2020 Hyperbolic Interaction Model for Hierarchical Multi-Label Classification Boli Chen, Xin Huang, Lin Xiao, Zixin Cai, Liping Jing

ICML 2020 Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, Laurent Massoulie

ICML 2019 A Composite Randomized Incremental Gradient Method Junyu Zhang, Lin Xiao

NeurIPS 2019 A Stochastic Composite Gradient Method with Incremental Variance Reduction Junyu Zhang, Lin Xiao

JMLR 2019 DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization Lin Xiao, Adams Wei Yu, Qihang Lin, Weizhu Chen

NeurIPS 2019 Understanding the Role of Momentum in Stochastic Gradient Methods Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao

NeurIPS 2019 Using Statistics to Automate Stochastic Optimization Hunter Lang, Lin Xiao, Pengchuan Zhang

NeurIPS 2018 Coupled Variational Bayes via Optimization Embedding Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

NeurIPS 2018 Learning SMaLL Predictors Vikas Garg, Ofer Dekel, Lin Xiao

ICML 2018 SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

UAI 2018 Sparse Multi-Prototype Classification Vikas K. Garg, Lin Xiao, Ofer Dekel

ICML 2017 Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms Jialei Wang, Lin Xiao

NeurIPS 2017 Q-LDA: Uncovering Latent Patterns in Text-Based Sequential Decision Processes Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng

JMLR 2017 Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization Yuchen Zhang, Lin Xiao

ICML 2017 Stochastic Variance Reduction Methods for Policy Evaluation Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou

NeurIPS 2015 End-to-End Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng

NeurIPS 2014 An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao

ICML 2014 An Adaptive Accelerated Proximal Gradient Method and Its Homotopy Continuation for Sparse Optimization Qihang Lin, Lin Xiao

AAAI 2014 Online Classification Using a Voted RDA Method Tianbing Xu, Jianfeng Gao, Lin Xiao, Amelia C. Regan

ICML 2012 A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem Lin Xiao, Tong Zhang

JMLR 2012 Optimal Distributed Online Prediction Using Mini-Batches Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao

ICML 2011 Hierarchical Classification via Orthogonal Transfer Lin Xiao, Dengyong Zhou, Mingrui Wu

ICML 2011 Optimal Distributed Online Prediction Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, Lin Xiao

JMLR 2010 Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization Lin Xiao

MLJ 2010 Learning to Classify with Missing and Corrupted Features Ofer Dekel, Ohad Shamir, Lin Xiao

COLT 2010 Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback Alekh Agarwal, Ofer Dekel, Lin Xiao

NeurIPS 2009 Dual Averaging Method for Regularized Stochastic Learning and Online Optimization Lin Xiao

ICML 2006 A Duality View of Spectral Methods for Dimensionality Reduction Lin Xiao, Jun Sun, Stephen P. Boyd