Lingxiao Wang

33 papers · 2016–2025 · 11 conferences · across top CS/AI conferences

Achievements

+13 more ↓

🗺️ Taxonomy Completionist (23) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 🌉 Interdisciplinary Bridge 🐣 Hot Topic Early Bird

🗺️ Taxonomy Completionist (23) 🧭 Keyword Pioneer 🏃 Academic Marathon (9) 🤝 Dynamic Duo (15) 👑 Triple Crown 🏆 Grand Slam 🔬 Deep Specialist (11) 🏆 Keyword Champion (2) 🔥 Unstoppable (10) ⚡ Prolific Year (5) 📈 Trend Setter 💎 Century Club (33) 🗃️ Keyword Collector (61)

Conferences

ICML (10) AISTATS (5) ICLR (5) NIPS (5) IJCAI (2) AAAI (1) COLT (1) CORL (1) JMLR (1) NAACL (1) UAI (1)

Top co-authors

Quanquan Gu (15) Zhaoran Wang (11) Zhuoran Yang (9) Xiao Zhang (5) Chenjia Bai (5) Qi Cai (3) Peng Liu (3) Kumar Kshitij Patel (3) Animesh Garg (3) Shuang Qiu (2)

Research topics

Differential Privacy (1) Statistics (1)

Keywords

nonconvex optimization (6) differential privacy (5) variance reduction (5) matrix completion (4) federated learning (3) online learning (3) low-rank matrix recovery (3) high-dimensional statistics (3) upper confidence bound (3) stochastic optimization (3) precision matrix estimation (3) linear convergence (3) gradient descent (2) deep reinforcement learning (2) stochastic gradient descent (2) reinforcement learning (2) distributed learning (2) non-convex optimization (2) distributed optimization (2) representation learning (2)

Papers

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback JMLR 2025 EVICheck: Evidence-Driven Independent Reasoning and Combined Verification Method for Fact-Checking IJCAI 2025 Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective CORL 2024 The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication COLT 2024 Efficient Privacy-Preserving Stochastic Nonconvex Optimization UAI 2023 Differentially Private Matrix Completion through Low-rank Matrix Factorization AISTATS 2023 Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics ICLR 2023 Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency ICLR 2023 Federated Online and Bandit Convex Optimization ICML 2023 Towards Optimal Communication Complexity in Distributed Non-Convex Optimization NIPS 2022 Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning ICML 2022 Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning ICLR 2022 Dynamic Bottleneck for Robust Self-Supervised Exploration NIPS 2021 Principled Exploration via Optimistic Bootstrapping and Backward Induction ICML 2021 Variance-reduced First-order Meta-learning for Natural Language Processing Tasks NAACL 2021 Provably Efficient Causal Reinforcement Learning with Confounded Observational Data NIPS 2021 Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning ICML 2020 Neural Policy Gradient Methods: Global Optimality and Rates of Convergence ICLR 2020 Improving Neural Language Generation with Spectrum Control ICLR 2020 A Knowledge Transfer Framework for Differentially Private Sparse Learning AAAI 2020 On the Global Optimality of Model-Agnostic Meta-Learning ICML 2020 Learning One-hidden-layer ReLU Networks via Gradient Descent AISTATS 2019 Statistical-Computational Tradeoff in Single Index Models NIPS 2019 Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning IJCAI 2019 A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery AISTATS 2018 Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization ICML 2018 A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery ICML 2018 Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NIPS 2018 High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm ICML 2017 A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery ICML 2017 Robust Gaussian Graphical Model Estimation with Arbitrary Corruption ICML 2017 A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation AISTATS 2017 Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates AISTATS 2016