Niao He

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 120

Top Venues

Publications

Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. AAAI (2024)
Florian Hübler, Junchi Yang, Xiang Li, Niao He
Parameter-Agnostic Optimization under Relaxed Smoothness. AISTATS (2024)
Jiawei Huang, Niao He, Andreas Krause
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. CoRR (2024)
Semih Cayci, Niao He, R. Srikant
Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. SIAM J. Optim. 34 (3) (2024)
Liang Zhang, Niao He, Michael Muehlebach
Primal Methods for Variational Inequality Problems with Functional Constraints. CoRR (2024)
Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He
Learning to Steer Markovian Agents under Model Uncertainty. CoRR (2024)
Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. CoRR (2024)
Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
Truly No-Regret Learning in Constrained MDPs. CoRR (2024)
Ilyas Fatkhullin, Niao He
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. CoRR (2024)
Ilyas Fatkhullin, Niao He
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. AISTATS (2024)
Philip Jordan, Anas Barakat, Niao He
Independent Learning in Constrained Markov Potential Games. AISTATS (2024)
Saber Salehkaleybar, Mohammadsadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran
Momentum-Based Policy Gradient with Second-Order Information. Trans. Mach. Learn. Res. 2024 (2024)
Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause
Provably Learning Nash Policies in Constrained Markov Potential Games. AAMAS (2024)
Batuhan Yardim, Artur Goldman, Niao He
When is Mean-Field Reinforcement Learning Tractable and Relevant? CoRR (2024)
Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. AISTATS (2024)
Philip Jordan, Anas Barakat, Niao He
Independent Learning in Constrained Markov Potential Games. CoRR (2024)
Yan Huang, Xiang Li, Yipeng Shen, Niao He, Jinming Xu
Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes. CoRR (2024)
Jiawei Huang, Batuhan Yardim, Niao He
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation. AISTATS (2024)
Batuhan Yardim, Artur Goldman, Niao He
When is Mean-Field Reinforcement Learning Tractable and Relevant? AAMAS (2024)
Xiang Li, Zebang Shen, Liang Zhang, Niao He
A Hessian-Aware Stochastic Differential Equation for Modelling SGD. CoRR (2024)
Semih Cayci, Niao He, R. Srikant
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. Trans. Mach. Learn. Res. 2024 (2024)
Ilyas Fatkhullin, Niao He, Yifan Hu
Stochastic Optimization under Hidden Convexity. CoRR (2024)
Anas Barakat, Ilyas Fatkhullin, Niao He
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML (2023)
Jiawei Huang, Niao He
Robust Knowledge Transfer in Tiered Reinforcement Learning. NeurIPS (2023)
Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai
Learning to Optimize with Stochastic Dominance Constraints. AISTATS (2023)
Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. NeurIPS (2023)
Kei Ishikawa, Niao He
Kernel Conditional Moment Constraints for Confounding Robust Inference. AISTATS (2023)
Vinzenz Thoma, Michael Curry, Niao He, Sven Seuken
Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning. CoRR (2023)
Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML (2023)
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist
On Imitation in Mean-field Games. CoRR (2023)
Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause
Provably Learning Nash Policies in Constrained Markov Potential Games. CoRR (2023)
Florian Hübler, Junchi Yang, Xiang Li, Niao He
Parameter-Agnostic Optimization under Relaxed Smoothness. CoRR (2023)
Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. ICML (2023)
Donghwan Lee, Jianghai Hu, Niao He
A Discrete-Time Switching System Analysis of Q-Learning. SIAM J. Control. Optim. 61 (3) (2023)
Liang Zhang, Junchi Yang, Amin Karbasi, Niao He
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. CoRR (2023)
Liang Zhang, Junchi Yang, Amin Karbasi, Niao He
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. NeurIPS (2023)
Jiawei Huang, Niao He
Robust Knowledge Transfer in Tiered Reinforcement Learning. CoRR (2023)
Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CDC (2023)
Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant
Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation. IEEE Trans. Autom. Control. 68 (5) (2023)
Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. Trans. Mach. Learn. Res. 2023 (2023)
Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. CoRR (2023)
Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization. CoRR (2023)
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist
On Imitation in Mean-field Games. NeurIPS (2023)
Jiawei Huang, Batuhan Yardim, Niao He
On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation. CoRR (2023)
Anas Barakat, Ilyas Fatkhullin, Niao He
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. CoRR (2023)
Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. CoRR (2023)
Adrian Müller, Pragnya Alatur, Giorgia Ramponi, Niao He
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes. CoRR (2023)
Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. CoRR (2023)
Kei Ishikawa, Niao He, Takafumi Kanamori
A Convex Framework for Confounding Robust Inference. CoRR (2023)
Ilyas Fatkhullin, Anas Barakat, Anastasya Kireeva, Niao He
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. CoRR (2023)
Xiang Li, Junchi Yang, Niao He
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. ICLR (2023)
Kei Ishikawa, Niao He
Kernel Conditional Moment Constraints for Confounding Robust Inference. CoRR (2023)
Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CoRR (2023)
Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. CoRR (2022)
Junchi Yang, Xiang Li, Niao He
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. NeurIPS (2022)
Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. AISTATS (2022)
Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems. CoRR (2022)
Liang Zhang, Kiran K. Thekumparampil, Sewoong Oh, Niao He
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. NeurIPS (2022)
Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran
Adaptive Momentum-Based Policy Gradient with Second-Order Information. CoRR (2022)
Semih Cayci, Niao He, R. Srikant
Learning to Control Partially Observed Systems with Finite Memory. CoRR (2022)
Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher
A Natural Actor-Critic Framework for Zero-Sum Markov Games. ICML (2022)
Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions. NeurIPS (2022)
Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS (2022)
Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran
Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions. CoRR (2022)
Xiang Li, Junchi Yang, Niao He
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. CoRR (2022)
Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. CoRR (2022)
Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. CoRR (2022)
Semih Cayci, Niao He, R. Srikant
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. CoRR (2022)
Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. NeurIPS (2022)
Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai
Learning to Optimize with Stochastic Dominance Constraints. CoRR (2022)
Junchi Yang, Xiang Li, Niao He
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. CoRR (2022)
Yifan Hu, Xin Chen, Niao He
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. NeurIPS (2021)
Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant
Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning. CoRR (2021)
Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR (2021)
Donghwan Lee, Niao He, Seungjae Lee, Panagiota Karava, Jianghai Hu
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction. CoRR (2021)
Semih Cayci, Niao He, R. Srikant
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. CoRR (2021)
Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization. CoRR (2021)
Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He
The complexity of nonconvex-strongly-concave minimax optimization. UAI (2021)
Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
Provably-Efficient Double Q-Learning. CoRR (2020)
Junchi Yang, Negar Kiyavash, Niao He
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems. CoRR (2020)
Donghwan Lee, Niao He
Periodic Q-Learning. CoRR (2020)
Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher
Optimization for Reinforcement Learning: From a single agent to cooperative agents. IEEE Signal Process. Mag. 37 (3) (2020)
Yifan Hu, Siqi Zhang, Xin Chen, Niao He
Biased Stochastic Gradient Descent for Conditional Stochastic Optimization. CoRR (2020)
Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He
A Catalyst Framework for Minimax Optimization. NeurIPS (2020)
Yifan Hu, Xin Chen, Niao He
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization. SIAM J. Optim. 30 (3) (2020)
Donghwan Lee, Niao He
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. NeurIPS (2020)
Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
The Mean-Squared Error of Double Q-Learning. NeurIPS (2020)
Junchi Yang, Negar Kiyavash, Niao He
Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. NeurIPS (2020)
Donghwan Lee, Niao He
Periodic Q-Learning. L4DC (2020)
Yifan Hu, Siqi Zhang, Xin Chen, Niao He
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. NeurIPS (2020)
Pan Li, Niao He, Olgica Milenkovic
Quadratic Decomposable Submodular Function Minimization: Theory and Practice. J. Mach. Learn. Res. 21 (2020)
Yingxiang Yang, Negar Kiyavash, Le Song, Niao He
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. NeurIPS (2020)
Harsh Gupta, Niao He, R. Srikant
Optimization and Learning Algorithms for Stochastic and Adversarial Power Control. WiOpt (2019)
Donghwan Lee, Niao He
Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs. ACC (2019)
Donghwan Lee, Niao He
Target-Based Temporal Difference Learning. CoRR (2019)
Donghwan Lee, Niao He
Target-Based Temporal-Difference Learning. ICML (2019)
Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS (2019)
Donghwan Lee, Niao He, Jianghai Hu
Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces. ACC (2019)
Donghwan Lee, Niao He
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms. CoRR (2019)
Pan Li, Niao He, Olgica Milenkovic
Quadratic Decomposable Submodular Function Minimization: Theory and Practice. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Niao He