Wang, Lingxiao

36 publications

JMLR 2025 Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao, Lingxiao Wang, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Mladen Kolar

IJCAI 2025 EVICheck: Evidence-Driven Independent Reasoning and Combined Verification Method for Fact-Checking Lingxiao Wang, Lei Shi, Feifei Kou, Ligu Zhu, Chen Ma, Pengfei Zhang, Mingying Xu, Zeyu Li

NeurIPS 2025 Revisiting Consensus Error: A Fine-Grained Analysis of Local SGD Under Second-Order Data Heterogeneity Kumar Kshitij Patel, Ali Zindari, Sebastian U Stich, Lingxiao Wang

CoRL 2024 Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective Haoran He, Peilin Wu, Chenjia Bai, Hang Lai, Lingxiao Wang, Ling Pan, Xiaolin Hu, Weinan Zhang

ICLRW 2024 Efficient Private Federated Non-Convex Optimization with Shuffled Model Lingxiao Wang, Xingyu Zhou, Kumar Kshitij Patel, Lawrence Tang, Aadirupa Saha

COLT 2024 The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro

AISTATS 2023 Differentially Private Matrix Completion Through Low-Rank Matrix Factorization Lingxiao Wang, Boxin Zhao, Mladen Kolar

UAI 2023 Efficient Privacy-Preserving Stochastic Nonconvex Optimization Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu

ICML 2023 Federated Online and Bandit Convex Optimization Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro

ICMLW 2023 On the Still Unreasonable Effectiveness of Federated Averaging for Heterogeneous Distributed Learning Kumar Kshitij Patel, Margalit Glasgow, Lingxiao Wang, Nirmit Joshi, Nathan Srebro

ICLR 2023 Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang, Csaba Szepesvari, Zhaoran Wang

ICLR 2023 Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

ICML 2022 Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang

NeurIPSW 2022 Distributed Online and Bandit Convex Optimization Kumar Kshitij Patel, Aadirupa Saha, Lingxiao Wang, Nathan Srebro

ICLR 2022 Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang

NeurIPS 2022 Towards Optimal Communication Complexity in Distributed Non-Convex Optimization Kumar Kshitij Patel, Lingxiao Wang, Blake E Woodworth, Brian Bullins, Nati Srebro

NeurIPS 2021 Dynamic Bottleneck for Robust Self-Supervised Exploration Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang

ICML 2021 Principled Exploration via Optimistic Bootstrapping and Backward Induction Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang

NeurIPS 2021 Provably Efficient Causal Reinforcement Learning with Confounded Observational Data Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

AAAI 2020 A Knowledge Transfer Framework for Differentially Private Sparse Learning Lingxiao Wang, Quanquan Gu

ICML 2020 Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

ICLR 2020 Improving Neural Language Generation with Spectrum Control Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu

ICLR 2020 Neural Policy Gradient Methods: Global Optimality and Rates of Convergence Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

ICML 2020 On the Global Optimality of Model-Agnostic Meta-Learning Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

IJCAI 2019 Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning Lingxiao Wang, Quanquan Gu

AISTATS 2019 Learning One-Hidden-Layer ReLU Networks via Gradient Descent Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu

NeurIPS 2019 Statistical-Computational Tradeoff in Single Index Models Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

ICML 2018 A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu

AISTATS 2018 A Unified Framework for Nonconvex Low-Rank Plus Sparse Matrix Recovery Xiao Zhang, Lingxiao Wang, Quanquan Gu

ICML 2018 Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu

NeurIPS 2018 Distributed Learning Without Distress: Privacy-Preserving Empirical Risk Minimization Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu

AISTATS 2017 A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation Lingxiao Wang, Xiao Zhang, Quanquan Gu

ICML 2017 A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery Lingxiao Wang, Xiao Zhang, Quanquan Gu

ICML 2017 High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu

ICML 2017 Robust Gaussian Graphical Model Estimation with Arbitrary Corruption Lingxiao Wang, Quanquan Gu

AISTATS 2016 Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates Lingxiao Wang, Xiang Ren, Quanquan Gu