| 2025 | AISTATS | From Gradient Clipping to Normalization for Heavy Tailed SGD. | Florian Hbler, Ilyas Fatkhullin, Niao He |
| 2025 | AISTATS | Steering No-Regret Agents in MFGs under Model Uncertainty. | Leo Widmer, Jiawei Huang, Niao He |
| 2025 | ICLR | Learning to Steer Markovian Agents under Model Uncertainty. | Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He |
| 2025 | ICLR | On the Crucial Role of Initialization for Matrix Factorization. | Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He |
| 2025 | ICML | Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective. | Jiawei Huang, Bingcong Li, Christoph Dann, Niao He |
| 2025 | ICML | Provable Maximum Entropy Manifold Exploration via Diffusion Models. | Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause |
| 2025 | UAI | Efficiently Escaping Saddle Points for Policy Optimization. | Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser |
| 2024 | AAAI | Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. | Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken |
| 2024 | AISTATS | Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. | Ilyas Fatkhullin, Niao He |
| 2024 | AISTATS | On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation. | Jiawei Huang, Batuhan Yardim, Niao He |
| 2024 | AISTATS | Parameter-Agnostic Optimization under Relaxed Smoothness. | Florian Hbler, Junchi Yang, Xiang Li, Niao He |
| 2024 | AISTATS | Independent Learning in Constrained Markov Potential Games. | Philip Jordan, Anas Barakat, Niao He |
| 2024 | AISTATS | Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. | Siqi Zhang, Yifan Hu, Liang Zhang, Niao He |
| 2024 | ICML | Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. | Jiawei Huang, Niao He, Andreas Krause |
| 2024 | ICML | Truly No-Regret Learning in Constrained MDPs. | Adrian Mller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He |
| 2024 | ICML | DPZero: Private Fine-Tuning of Language Models without Backpropagation. | Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He |
| 2023 | AISTATS | Learning to Optimize with Stochastic Dominance Constraints. | Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai |
| 2023 | AISTATS | Kernel Conditional Moment Constraints for Confounding Robust Inference. | Kei Ishikawa, Niao He |
| 2023 | ICLR | TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. | Xiang Li, Junchi Yang, Niao He |
| 2023 | ICML | Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. | Anas Barakat, Ilyas Fatkhullin, Niao He |
| 2023 | ICML | Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. | Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He |
| 2023 | ICML | Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. | Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He |
| 2022 | AISTATS | Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. | Kiran Koshy Thekumparampil, Niao He, Sewoong Oh |
| 2022 | AISTATS | Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. | Junchi Yang, Antonio Orvieto, Aurlien Lucchi, Niao He |
| 2022 | ICML | A Natural Actor-Critic Framework for Zero-Sum Markov Games. | Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher |
| 2021 | UAI | The complexity of nonconvex-strongly-concave minimax optimization. | Siqi Zhang, Junchi Yang, Cristbal Guzmn, Negar Kiyavash, Niao He |
| 2019 | AISTATS | Kernel Exponential Family Estimation via Doubly Dual Embedding. | Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He |
| 2019 | ICML | Target-Based Temporal-Difference Learning. | Donghwan Lee, Niao He |
| 2019 | WiOpt | Optimization and Learning Algorithms for Stochastic and Adversarial Power Control. | Harsh Gupta, Niao He, R. Srikant |
| 2018 | ICLR | Boosting the Actor with Dual Critic. | Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song |
| 2018 | ICML | SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. | Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song |
| 2017 | AISTATS | Learning from Conditional Distributions via Dual Embeddings. | Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song |
| 2017 | ICML | Stochastic Generative Hashing. | Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song |
| 2016 | AISTATS | Provable Bayesian Inference via Particle Mirror Descent. | Bo Dai, Niao He, Hanjun Dai, Le Song |
| 2013 | ICML | Stochastic Alternating Direction Method of Multipliers. | Hua Ouyang, Niao He, Long Q. Tran, Alexander G. Gray |