| 2025 | IJCAI | 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 |
| 2024 | COLT | 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 |
| 2024 | CoRL | 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 |
| 2024 | ICPADS | Design and Optimization of Smart Contracts for Cross-Domain Sharing of Sensitive Data. | Manqing Zhu, Lingxiao Wang, Xiaohong Li |
| 2023 | AISTATS | Differentially Private Matrix Completion through Low-rank Matrix Factorization. | Lingxiao Wang, Boxin Zhao, Mladen Kolar |
| 2023 | ICLR | Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. | Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2023 | ICLR | Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics. | Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang, Csaba Szepesvri, Zhaoran Wang |
| 2023 | ICML | Federated Online and Bandit Convex Optimization. | Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro |
| 2023 | UAI | Efficient Privacy-Preserving Stochastic Nonconvex Optimization. | Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu |
| 2022 | ICLR | Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. | Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang |
| 2022 | ICML | Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. | Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang |
| 2022 | INFOCOM | Demo Abstract: Environment-adaptive 3D Human Pose Tracking with RFID. | Chao Yang, Lingxiao Wang, Xuyu Wang, Shiwen Mao |
| 2021 | GLOBECOM | Meta-Pose: Environment-adaptive Human Skeleton Tracking with RFID. | Chao Yang, Lingxiao Wang, Xuyu Wang, Shiwen Mao |
| 2021 | ICML | Principled Exploration via Optimistic Bootstrapping and Backward Induction. | Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang |
| 2021 | ICMLA | Learn to Trace Odors: Autonomous Odor Source Localization via Deep Learning Methods. | Lingxiao Wang, Shuo Pang, Jinlong Li |
| 2021 | NAACL | Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. | Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang |
| 2020 | AAAI | A Knowledge Transfer Framework for Differentially Private Sparse Learning. | Lingxiao Wang, Quanquan Gu |
| 2020 | ICLR | Improving Neural Language Generation with Spectrum Control. | Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu |
| 2020 | ICLR | Neural Policy Gradient Methods: Global Optimality and Rates of Convergence. | Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2020 | ICML | On the Global Optimality of Model-Agnostic Meta-Learning. | Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2020 | ICML | Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning. | Lingxiao Wang, Zhuoran Yang, Zhaoran Wang |
| 2020 | IROS | An Implementation of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for Odor Source Localization. | Lingxiao Wang, Shuo Pang |
| 2019 | AISTATS | Learning One-hidden-layer ReLU Networks via Gradient Descent. | Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu |
| 2019 | IJCAI | Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. | Lingxiao Wang, Quanquan Gu |
| 2018 | AISTATS | A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. | Xiao Zhang, Lingxiao Wang, Quanquan Gu |
| 2018 | ICIP | Feature Learning for One-Shot Face Recognition. | Lingxiao Wang, Yali Li, Shengjin Wang |
| 2018 | ICML | Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. | Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu |
| 2018 | ICML | A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. | Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu |
| 2017 | AISTATS | A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. | Lingxiao Wang, Xiao Zhang, Quanquan Gu |
| 2017 | ICML | Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. | Lingxiao Wang, Quanquan Gu |
| 2017 | ICML | A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. | Lingxiao Wang, Xiao Zhang, Quanquan Gu |
| 2017 | ICML | High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. | Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu |
| 2016 | AISTATS | Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. | Lingxiao Wang, Xiang Ren, Quanquan Gu |