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Niao He

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 14, 2026, 10:11 PM AMS

23

papers

263

total citations

papers (23)

Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning

NEURIPS 2020arXiv

Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods

NEURIPS 2023arXiv

Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization

NEURIPS 2022arXiv

Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization

NEURIPS 2022arXiv

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions

NEURIPS 2022arXiv

DPZero: Private Fine-Tuning of Language Models without Backpropagation

The Mean-Squared Error of Double Q-Learning

NEURIPS 2020arXiv

Truly No-Regret Learning in Constrained MDPs

On the Crucial Role of Initialization for Matrix Factorization

Automated Design of Affine Maximizer Mechanisms in Dynamic Settings

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

On Imitation in Mean-field Games

NEURIPS 2023arXiv

Provable Maximum Entropy Manifold Exploration via Diffusion Models

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization

NEURIPS 2023arXiv

Robust Knowledge Transfer in Tiered Reinforcement Learning

NEURIPS 2023arXiv

A Catalyst Framework for Minimax Optimization

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality

On the Bias-Variance-Cost Tradeoff of Stochastic Optimization

Scalable Neural Incentive Design with Parameterized Mean-Field Approximation

NEURIPS 2025arXiv

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models

A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms