Niao He

A Convex Framework for Confounding Robust Inference JMLR 2026 Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective ICML 2025 Efficiently Escaping Saddle Points for Policy Optimization UAI 2025 Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning L4DC 2025 From Gradient Clipping to Normalization for Heavy Tailed SGD AISTATS 2025 Learning to Steer Markovian Agents under Model Uncertainty ICLR 2025 On the Crucial Role of Initialization for Matrix Factorization ICLR 2025 Provable Maximum Entropy Manifold Exploration via Diffusion Models ICML 2025 Steering No-Regret Agents in MFGs under Model Uncertainty AISTATS 2025 Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes NeurIPS 2024 Automated Design of Affine Maximizer Mechanisms in Dynamic Settings AAAI 2024 DPZero: Private Fine-Tuning of Language Models without Backpropagation ICML 2024 Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization AISTATS 2024 Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems NeurIPS 2024 Independent Learning in Constrained Markov Potential Games AISTATS 2024 Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL ICML 2024 On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation AISTATS 2024 Parameter-Agnostic Optimization under Relaxed Smoothness AISTATS 2024 Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence AISTATS 2024 Truly No-Regret Learning in Constrained MDPs ICML 2024 Kernel Conditional Moment Constraints for Confounding Robust Inference AISTATS 2023 Learning to Optimize with Stochastic Dominance Constraints AISTATS 2023 On Imitation in Mean-field Games NeurIPS 2023 Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization NeurIPS 2023 Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games ICML 2023 Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space ICML 2023 Robust Knowledge Transfer in Tiered Reinforcement Learning NeurIPS 2023 Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies ICML 2023 TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization ICLR 2023 Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods NeurIPS 2023 A Natural Actor-Critic Framework for Zero-Sum Markov Games ICML 2022 Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization NeurIPS 2022 Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity AISTATS 2022 Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization AISTATS 2022 Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization NeurIPS 2022 Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality NeurIPS 2022 Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions NeurIPS 2022 On the Bias-Variance-Cost Tradeoff of Stochastic Optimization NeurIPS 2021 The complexity of nonconvex-strongly-concave minimax optimization UAI 2021 A Catalyst Framework for Minimax Optimization NeurIPS 2020 A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms NeurIPS 2020 Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning NeurIPS 2020 Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems NeurIPS 2020 Periodic Q-Learning L4DC 2020 Quadratic Decomposable Submodular Function Minimization: Theory and Practice JMLR 2020 The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models NeurIPS 2020 The Mean-Squared Error of Double Q-Learning NeurIPS 2020 Exponential Family Estimation via Adversarial Dynamics Embedding NeurIPS 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019 Learning Positive Functions with Pseudo Mirror Descent NeurIPS 2019 Target-Based Temporal-Difference Learning ICML 2019 Boosting the Actor with Dual Critic ICLR 2018 Coupled Variational Bayes via Optimization Embedding NeurIPS 2018 Predictive Approximate Bayesian Computation via Saddle Points NeurIPS 2018 Quadratic Decomposable Submodular Function Minimization NeurIPS 2018 SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation ICML 2018 Learning from Conditional Distributions via Dual Embeddings AISTATS 2017 Online Learning for Multivariate Hawkes Processes NeurIPS 2017 Stochastic Generative Hashing ICML 2017 Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016 Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization NeurIPS 2015 Time-Sensitive Recommendation From Recurrent User Activities NeurIPS 2015 Scalable Kernel Methods via Doubly Stochastic Gradients NeurIPS 2014 Stochastic Alternating Direction Method of Multipliers ICML 2013