Niao He

According to our database, Niao He authored at least 89 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Primal Methods for Variational Inequality Problems with Functional Constraints.

Liang Zhang, Niao He, Michael Muehlebach

CoRR, 2024

Independent Learning in Constrained Markov Potential Games.

Philip Jordan, Anas Barakat, Niao He

CoRR, 2024

Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence.

Ilyas Fatkhullin, Niao He

CoRR, 2024

Truly No-Regret Learning in Constrained MDPs.

Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He

CoRR, 2024

When is Mean-Field Reinforcement Learning Tractable and Relevant?

Batuhan Yardim, Artur Goldman, Niao He

CoRR, 2024

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

Jiawei Huang, Niao He, Andreas Krause

CoRR, 2024

Stochastic Optimization under Hidden Convexity.

Ilyas Fatkhullin, Niao He, Yifan Hu

CoRR, 2024

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

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

A Discrete-Time Switching System Analysis of Q-Learning.

Donghwan Lee, Jianghai Hu, Niao He

SIAM J. Control. Optim., June, 2023

Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation.

Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant

IEEE Trans. Autom. Control., May, 2023

Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning.

Vinzenz Thoma, Michael J. Curry, Niao He, Sven Seuken

CoRR, 2023

Efficiently Escaping Saddle Points for Non-Convex Policy Optimization.

Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser

CoRR, 2023

Parameter-Agnostic Optimization under Relaxed Smoothness.

Florian Hübler, Junchi Yang, Xiang Li, Niao He

CoRR, 2023

DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization.

Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He

CoRR, 2023

A Convex Framework for Confounding Robust Inference.

Kei Ishikawa, Niao He, Takafumi Kanamori

CoRR, 2023

Provably Convergent Policy Optimization via Metric-aware Trust Region Methods.

Jun Song, Niao He, Lijun Ding, Chaoyue Zhao

CoRR, 2023

Provably Learning Nash Policies in Constrained Markov Potential Games.

Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause

CoRR, 2023

Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes.

Adrian Müller, Pragnya Alatur, Giorgia Ramponi, Niao He

CoRR, 2023

On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation.

Jiawei Huang, Batuhan Yardim, Niao He

CoRR, 2023

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization.

Liang Zhang, Junchi Yang, Amin Karbasi, Niao He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Imitation in Mean-field Games.

Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Robust Knowledge Transfer in Tiered Reinforcement Learning.

Jiawei Huang, Niao He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games.

Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He

Proceedings of the International Conference on Machine Learning, 2023

Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies.

Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He

Proceedings of the International Conference on Machine Learning, 2023

Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space.

Anas Barakat, Ilyas Fatkhullin, Niao He

Proceedings of the International Conference on Machine Learning, 2023

TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization.

Xiang Li, Junchi Yang, Niao He

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity.

Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

Kernel Conditional Moment Constraints for Confounding Robust Inference.

Kei Ishikawa, Niao He

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Learning to Optimize with Stochastic Dominance Constraints.

Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Learning to Optimize with Stochastic Dominance Constraints.

Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai

CoRR, 2022

Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm.

Semih Cayci, Niao He, R. Srikant

CoRR, 2022

Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems.

Siqi Zhang, Yifan Hu, Liang Zhang, Niao He

CoRR, 2022

Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions.

Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran

CoRR, 2022

Adaptive Momentum-Based Policy Gradient with Second-Order Information.

Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran

CoRR, 2022

Learning to Control Partially Observed Systems with Finite Memory.

Semih Cayci, Niao He, R. Srikant

CoRR, 2022

Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization.

Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization.

Junchi Yang, Xiang Li, Niao He

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality.

Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Natural Actor-Critic Framework for Zero-Sum Markov Games.

Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher

Proceedings of the International Conference on Machine Learning, 2022

Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity.

Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization.

Kiran Koshy Thekumparampil, Niao He, Sewoong Oh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation.

Semih Cayci, Niao He, R. Srikant

CoRR, 2021

Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction.

Donghwan Lee, Niao He, Seungjae Lee, Panagiota Karava, Jianghai Hu

CoRR, 2021

Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning.

Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant

CoRR, 2021

The complexity of nonconvex-strongly-concave minimax optimization.

Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

On the Bias-Variance-Cost Tradeoff of Stochastic Optimization.

Yifan Hu, Xin Chen, Niao He

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Optimization for Reinforcement Learning: From a single agent to cooperative agents.

Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher

IEEE Signal Process. Mag., 2020

Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization.

Yifan Hu, Xin Chen, Niao He

SIAM J. Optim., 2020

Quadratic Decomposable Submodular Function Minimization: Theory and Practice.

Pan Li, Niao He, Olgica Milenkovic

J. Mach. Learn. Res., 2020

Provably-Efficient Double Q-Learning.

Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

CoRR, 2020

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization.

Yifan Hu, Siqi Zhang, Xin Chen, Niao He

CoRR, 2020

Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems.

Junchi Yang, Negar Kiyavash, Niao He

CoRR, 2020

A Catalyst Framework for Minimax Optimization.

Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Yingxiang Yang, Negar Kiyavash, Le Song, Niao He

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Junchi Yang, Negar Kiyavash, Niao He

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Mean-Squared Error of Double Q-Learning.

Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Yifan Hu, Siqi Zhang, Xin Chen, Niao He

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Donghwan Lee, Niao He

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Periodic Q-Learning.

Donghwan Lee, Niao He

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

2019

A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms.

Donghwan Lee, Niao He

CoRR, 2019

Optimization and Learning Algorithms for Stochastic and Adversarial Power Control.

Harsh Gupta, Niao He, R. Srikant

Proceedings of the International Symposium on Modeling and Optimization in Mobile, 2019

Learning Positive Functions with Pseudo Mirror Descent.

Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exponential Family Estimation via Adversarial Dynamics Embedding.

Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Target-Based Temporal-Difference Learning.

Donghwan Lee, Niao He

Proceedings of the 36th International Conference on Machine Learning, 2019

Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces.

Donghwan Lee, Niao He, Jianghai Hu

Proceedings of the 2019 American Control Conference, 2019

Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs.

Donghwan Lee, Niao He

Proceedings of the 2019 American Control Conference, 2019

Kernel Exponential Family Estimation via Doubly Dual Embedding.

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Predictive Approximate Bayesian Computation via Saddle Points.

Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Quadratic Decomposable Submodular Function Minimization.

Pan Li, Niao He, Olgica Milenkovic

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Coupled Variational Bayes via Optimization Embedding.

Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Proceedings of the 35th International Conference on Machine Learning, 2018

Boosting the Actor with Dual Critic.

Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Smoothed Dual Embedding Control.

Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song

CoRR, 2017

Online Learning for Multivariate Hawkes Processes.

Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stochastic Generative Hashing.

Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning from Conditional Distributions via Dual Embeddings.

Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Fast and Simple Optimization for Poisson Likelihood Models.

Niao He, Zaïd Harchaoui, Yichen Wang, Le Song

CoRR, 2016

Learning from Conditional Distributions via Dual Kernel Embeddings.

Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song

CoRR, 2016

Provable Bayesian Inference via Particle Mirror Descent.

Bo Dai, Niao He, Hanjun Dai, Le Song

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Scalable Bayesian Inference via Particle Mirror Descent.

Bo Dai, Niao He, Hanjun Dai, Le Song

CoRR, 2015

Mirror Prox algorithm for multi-term composite minimization and semi-separable problems.

Niao He, Anatoli B. Juditsky, Arkadi Nemirovski

Comput. Optim. Appl., 2015

Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization.

Niao He, Zaïd Harchaoui

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Time-Sensitive Recommendation From Recurrent User Activities.

Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Scalable Kernel Methods via Doubly Stochastic Gradients.

Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Mirror Prox Algorithm for Multi-Term Composite Minimization and Alternating Directions.

Niao He, Anatoli B. Juditsky, Arkadi Nemirovski

CoRR, 2013

Stochastic Alternating Direction Method of Multipliers.

Hua Ouyang, Niao He, Long Q. Tran, Alexander G. Gray

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Stochastic ADMM for Nonsmooth Optimization

Hua Ouyang, Niao He, Alexander G. Gray

CoRR, 2012