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Niao He

63 papers · 2013–2025 · 8 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) πŸ—ΊοΈ Taxonomy Completionist (15) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (9) 🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (8) 🏠 Conference Loyalist (26) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (28) 🀝 Dynamic Duo (11) πŸ† Keyword Champion (4) πŸ“ˆ Trend Setter ❓ The Questioner ⚑ Prolific Year (8) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (67) πŸ’Ž Century Club (63) πŸ”₯ Unstoppable (13)

Conferences

NIPS (26) AISTATS (14) ICML (13) ICLR (4) L4DC (2) UAI (2) AAAI (1) JMLR (1)

Top co-authors

Le Song (11) Bo Dai (11) Negar Kiyavash (10) Junchi Yang (9) Liang Zhang (6) Jiawei Huang (6) Ilyas Fatkhullin (6) Xiang Li (5) Hanjun Dai (5) Siqi Zhang (4)

Research topics

Methods (1) Differential Privacy (1)

Keywords

sample complexity (12) stochastic optimization (11) minimax optimization (10) convex optimization (8) reinforcement learning (7) stochastic gradient descent (6) nonconvex optimization (6) nash equilibrium (5) variance reduction (5) convergence rate (4) gradient descent ascent (3) stochastic gradient (3) function approximation (3) policy gradient (3) maximum likelihood estimation (3) reproducing kernel hilbert space (3) linear function approximation (3) online learning (2) multi-agent reinforcement learning (2) markov decision process (2)

Papers

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