Stochastic ADMM for Nonsmooth Optimization - Hua Ouyang; Niao He; Alexander G. Gray (CoRR, 2012)

Stochastic Alternating Direction Method of Multipliers. - Hua Ouyang; Niao He; Long Q. Tran; Alexander G. Gray (ICML (1), 2013)

Mirror Prox Algorithm for Multi-Term Composite Minimization and Alternating Directions. - Niao He; Anatoli B. Juditsky; Arkadi Nemirovski (CoRR, 2013)

Scalable Kernel Methods via Doubly Stochastic Gradients. - Bo Dai 001; Bo Xie 002; Niao He; Yingyu Liang; Anant Raj; Maria-Florina Balcan; Le Song (NIPS, 2014)

Time-Sensitive Recommendation From Recurrent User Activities. - Nan Du 002; Yichen Wang 001; Niao He; Jimeng Sun 001; Le Song (NIPS, 2015)

Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization. - Niao He; Zaïd Harchaoui (NIPS, 2015)

Mirror Prox algorithm for multi-term composite minimization and semi-separable problems. - Niao He; Anatoli B. Juditsky; Arkadi Nemirovski (Comput. Optim. Appl., 2015)

Scalable Bayesian Inference via Particle Mirror Descent. - Bo Dai 001; Niao He; Hanjun Dai; Le Song (CoRR, 2015)

Provable Bayesian Inference via Particle Mirror Descent. - Bo Dai 001; Niao He; Hanjun Dai; Le Song (AISTATS, 2016)

Learning from Conditional Distributions via Dual Kernel Embeddings. - Bo Dai 001; Niao He; Yunpeng Pan; Byron Boots; Le Song (CoRR, 2016)

Fast and Simple Optimization for Poisson Likelihood Models. - Niao He; Zaïd Harchaoui; Yichen Wang 001; Le Song (CoRR, 2016)

Learning from Conditional Distributions via Dual Embeddings. - Bo Dai 001; Niao He; Yunpeng Pan; Byron Boots; Le Song (AISTATS, 2017)

Stochastic Generative Hashing. - Bo Dai 001; Ruiqi Guo; Sanjiv Kumar; Niao He; Le Song (ICML, 2017)

Online Learning for Multivariate Hawkes Processes. - Yingxiang Yang; Jalal Etesami; Niao He; Negar Kiyavash (NIPS, 2017)

Smoothed Dual Embedding Control. - Bo Dai 001; Albert E. Shaw; Lihong Li 001; Lin Xiao; Niao He; Jianshu Chen; Le Song (CoRR, 2017)

Boosting the Actor with Dual Critic. - Bo Dai 001; Albert E. Shaw; Niao He; Lihong Li 001; Le Song (ICLR (Poster), 2018)

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. - Bo Dai 001; Albert E. Shaw; Lihong Li 001; Lin Xiao; Niao He; Zhen Liu 019; Jianshu Chen; Le Song (ICML, 2018)

Coupled Variational Bayes via Optimization Embedding. - Bo Dai 001; Hanjun Dai; Niao He; Weiyang Liu; Zhen Liu 019; Jianshu Chen; Lin Xiao; Le Song (NeurIPS, 2018)

Quadratic Decomposable Submodular Function Minimization. - Pan Li 005; Niao He; Olgica Milenkovic (NeurIPS, 2018)

Predictive Approximate Bayesian Computation via Saddle Points. - Yingxiang Yang; Bo Dai 001; Negar Kiyavash; Niao He (NeurIPS, 2018)

Kernel Exponential Family Estimation via Doubly Dual Embedding. - Bo Dai 001; Hanjun Dai; Arthur Gretton; Le Song; Dale Schuurmans; Niao He (AISTATS, 2019)

Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs. - Donghwan Lee 002; Niao He (ACC, 2019)

Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces. - Donghwan Lee 002; Niao He; Jianghai Hu (ACC, 2019)

Target-Based Temporal-Difference Learning. - Donghwan Lee 002; Niao He (ICML, 2019)

Exponential Family Estimation via Adversarial Dynamics Embedding. - Bo Dai 001; Zhen Liu 019; Hanjun Dai; Niao He; Arthur Gretton; Le Song; Dale Schuurmans (NeurIPS, 2019)

Learning Positive Functions with Pseudo Mirror Descent. - Yingxiang Yang; Haoxiang Wang; Negar Kiyavash; Niao He (NeurIPS, 2019)

Optimization and Learning Algorithms for Stochastic and Adversarial Power Control. - Harsh Gupta; Niao He; R. Srikant 001 (WiOpt, 2019)

A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms. - Donghwan Lee 002; Niao He (CoRR, 2019)

Periodic Q-Learning. - Donghwan Lee 002; Niao He (L4DC, 2020)

A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. - Donghwan Lee 002; Niao He (NeurIPS, 2020)

Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. - Yifan Hu; Siqi Zhang; Xin Chen; Niao He (NeurIPS, 2020)

The Mean-Squared Error of Double Q-Learning. - Wentao Weng; Harsh Gupta; Niao He; Lei Ying 001; R. Srikant 001 (NeurIPS, 2020)

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. - Junchi Yang; Negar Kiyavash; Niao He (NeurIPS, 2020)

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. - Yingxiang Yang; Negar Kiyavash; Le Song; Niao He (NeurIPS, 2020)

A Catalyst Framework for Minimax Optimization. - Junchi Yang; Siqi Zhang; Negar Kiyavash; Niao He (NeurIPS, 2020)

Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems. - Junchi Yang; Negar Kiyavash; Niao He (CoRR, 2020)

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization. - Yifan Hu; Siqi Zhang; Xin Chen; Niao He (CoRR, 2020)

Provably-Efficient Double Q-Learning. - Wentao Weng; Harsh Gupta; Niao He; Lei Ying 001; R. Srikant 001 (CoRR, 2020)

Quadratic Decomposable Submodular Function Minimization: Theory and Practice. - Pan Li 005; Niao He; Olgica Milenkovic (J. Mach. Learn. Res., 2020)

Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization. - Yifan Hu; Xin Chen; Niao He (SIAM J. Optim., 2020)

Optimization for Reinforcement Learning: From a single agent to cooperative agents. - Donghwan Lee 002; Niao He; Parameswaran Kamalaruban; Volkan Cevher (IEEE Signal Process. Mag., 2020)

On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. - Yifan Hu; Xin Chen; Niao He (NeurIPS, 2021)

The complexity of nonconvex-strongly-concave minimax optimization. - Siqi Zhang; Junchi Yang; Cristóbal Guzmán; Negar Kiyavash; Niao He (UAI, 2021)

Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning. - Semih Cayci; Siddhartha Satpathi; Niao He; R. Srikant 001 (CoRR, 2021)

Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction. - Donghwan Lee 002; Niao He; Seungjae Lee 003; Panagiota Karava; Jianghai Hu (CoRR, 2021)

Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. - Semih Cayci; Niao He; R. Srikant 001 (CoRR, 2021)

Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. - Kiran Koshy Thekumparampil; Niao He; Sewoong Oh (AISTATS, 2022)

Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. - Junchi Yang; Antonio Orvieto; Aurélien Lucchi; Niao He (AISTATS, 2022)

A Natural Actor-Critic Framework for Zero-Sum Markov Games. - Ahmet Alacaoglu; Luca Viano; Niao He; Volkan Cevher (ICML, 2022)

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. - Ilyas Fatkhullin; Jalal Etesami; Niao He; Negar Kiyavash (NeurIPS, 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 (NeurIPS, 2022)

Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. - Junchi Yang; Xiang Li; Niao He (NeurIPS, 2022)

Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. - Liang Zhang; Kiran Koshy Thekumparampil; Sewoong Oh; Niao He (NeurIPS, 2022)

Learning to Control Partially Observed Systems with Finite Memory. - Semih Cayci; Niao He; R. Srikant 001 (CoRR, 2022)

Adaptive Momentum-Based Policy Gradient with Second-Order Information. - Saber Salehkaleybar; Sadegh Khorasani; Negar Kiyavash; Niao He; Patrick Thiran (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)

Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems. - Siqi Zhang; Yifan Hu; Liang Zhang; Niao He (CoRR, 2022)

Learning to Optimize with Stochastic Dominance Constraints. - Hanjun Dai; Yuan Xue; Niao He; Bethany Wang; Na Li 002; Dale Schuurmans; Bo Dai 001 (CoRR, 2022)

Learning to Optimize with Stochastic Dominance Constraints. - Hanjun Dai; Yuan Xue; Niao He; Yixin Wang; Na Li 002; Dale Schuurmans; Bo Dai 001 (AISTATS, 2023)

Kernel Conditional Moment Constraints for Confounding Robust Inference. - Kei Ishikawa; Niao He (AISTATS, 2023)

Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. - Jiduan Wu; Anas Barakat; Ilyas Fatkhullin; Niao He (CDC, 2023)

TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. - Xiang Li; Junchi Yang; Niao He (ICLR, 2023)

Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. - Anas Barakat; Ilyas Fatkhullin; Niao He (ICML, 2023)

Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. - Ilyas Fatkhullin; Anas Barakat; Anastasia Kireeva; Niao He (ICML, 2023)

Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. - Batuhan Yardim; Semih Cayci; Matthieu Geist; Niao He (ICML, 2023)

Robust Knowledge Transfer in Tiered Reinforcement Learning. - Jiawei Huang; Niao He (NeurIPS, 2023)

On Imitation in Mean-field Games. - Giorgia Ramponi; Pavel Kolev; Olivier Pietquin; Niao He; Mathieu Laurière; Matthieu Geist (NeurIPS, 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 (NeurIPS, 2023)

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. - Liang Zhang; Junchi Yang; Amin Karbasi; Niao He (NeurIPS, 2023)

Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes. - Adrian Müller 002; Pragnya Alatur; Giorgia Ramponi; Niao He (CoRR, 2023)

A Convex Framework for Confounding Robust Inference. - Kei Ishikawa; Niao He; Takafumi Kanamori (CoRR, 2023)

DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. - Liang Zhang; Kiran Koshy Thekumparampil; Sewoong Oh; Niao He (CoRR, 2023)

Efficiently Escaping Saddle Points for Non-Convex Policy Optimization. - Sadegh Khorasani; Saber Salehkaleybar; Negar Kiyavash; Niao He; Matthias Grossglauser (CoRR, 2023)

Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning. - Vinzenz Thoma; Michael J. Curry; Niao He; Sven Seuken (CoRR, 2023)

Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. - Jun Song; Niao He; Lijun Ding; Chaoyue Zhao (Trans. Mach. Learn. Res., 2023)

Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation. - Semih Cayci; Siddhartha Satpathi; Niao He; R. Srikant 001 (IEEE Trans. Autom. Control., 2023)

A Discrete-Time Switching System Analysis of Q-Learning. - Donghwan Lee 002; Jianghai Hu; Niao He (SIAM J. Control. Optim., 2023)

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 (AAAI, 2024)

Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. - Ilyas Fatkhullin; Niao He (AISTATS, 2024)

On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation. - Jiawei Huang; Batuhan Yardim; Niao He (AISTATS, 2024)

Parameter-Agnostic Optimization under Relaxed Smoothness. - Florian Hübler; Junchi Yang; Xiang Li; Niao He (AISTATS, 2024)

Independent Learning in Constrained Markov Potential Games. - Philip Jordan; Anas Barakat; Niao He (AISTATS, 2024)

Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. - Siqi Zhang; Yifan Hu; Liang Zhang; Niao He (AISTATS, 2024)

Provably Learning Nash Policies in Constrained Markov Potential Games. - Pragnya Alatur; Giorgia Ramponi; Niao He; Andreas Krause 001 (AAMAS, 2024)

When is Mean-Field Reinforcement Learning Tractable and Relevant? - Batuhan Yardim; Artur Goldman; Niao He (AAMAS, 2024)

Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. - Pragnya Alatur; Anas Barakat; Niao He (CDC, 2024)

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. - Jiawei Huang; Niao He; Andreas Krause 001 (ICML, 2024)

Truly No-Regret Learning in Constrained MDPs. - Adrian Müller 002; Pragnya Alatur; Volkan Cevher; Giorgia Ramponi; Niao He (ICML, 2024)

DPZero: Private Fine-Tuning of Language Models without Backpropagation. - Liang Zhang; Bingcong Li; Kiran Koshy Thekumparampil; Sewoong Oh; Niao He (ICML, 2024)

Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes. - Yan Huang 036; Xiang Li; Yipeng Shen; Niao He; Jinming Xu 002 (NeurIPS, 2024)

Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems. - Bingcong Li; Liang Zhang; Niao He (NeurIPS, 2024)

Stochastic Optimization under Hidden Convexity. - Ilyas Fatkhullin; Niao He; Yifan Hu (CoRR, 2024)

Primal Methods for Variational Inequality Problems with Functional Constraints. - Liang Zhang; Niao He; Michael Muehlebach (CoRR, 2024)

A Hessian-Aware Stochastic Differential Equation for Modelling SGD. - Xiang Li; Zebang Shen; Liang Zhang; Niao He (CoRR, 2024)

Complexity of Minimizing Projected-Gradient-Dominated Functions with Stochastic First-order Oracles. - Saeed Masiha; Saber Salehkaleybar; Niao He; Negar Kiyavash; Patrick Thiran (CoRR, 2024)

Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles. - Yifan Hu; Jie Wang; Xin Chen; Niao He (CoRR, 2024)

Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. - Semih Cayci; Niao He; R. Srikant 001 (SIAM J. Optim., 2024)

Finite-Time Analysis of Natural Actor-Critic for POMDPs. - Semih Cayci; Niao He; R. Srikant 001 (SIAM J. Math. Data Sci., 2024)

Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. - Semih Cayci; Niao He; R. Srikant 001 (Trans. Mach. Learn. Res., 2024)

Momentum-Based Policy Gradient with Second-Order Information. - Saber Salehkaleybar; Mohammadsadegh Khorasani; Negar Kiyavash; Niao He; Patrick Thiran (Trans. Mach. Learn. Res., 2024)

From Gradient Clipping to Normalization for Heavy Tailed SGD. - Florian Hübler; Ilyas Fatkhullin; Niao He (AISTATS, 2025)

Steering No-Regret Agents in MFGs under Model Uncertainty. - Leo Widmer; Jiawei Huang; Niao He (AISTATS, 2025)

Learning to Steer Markovian Agents under Model Uncertainty. - Jiawei Huang; Vinzenz Thoma; Zebang Shen; Heinrich H. Nax; Niao He (ICLR, 2025)

On the Crucial Role of Initialization for Matrix Factorization. - Bingcong Li; Liang Zhang; Aryan Mokhtari; Niao He (ICLR, 2025)

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning. - Batuhan Yardim; Niao He (L4DC, 2025)

Efficiently Escaping Saddle Points for Policy Optimization. - Mohammadsadegh Khorasani; Saber Salehkaleybar; Negar Kiyavash; Niao He; Matthias Grossglauser (UAI, 2025)

Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management. - Xin Chen 026; Niao He; Yifan Hu; Zikun Ye (Oper. Res., 2025)

A Variational Inequality Approach to Independent Learning in Static Mean-Field Games. - Batuhan Yardim; Semih Cayci; Niao He (CoRR, 2025)

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective. - Jiawei Huang; Bingcong Li; Christoph Dann; Niao He (CoRR, 2025)

AmorLIP: Efficient Language-Image Pretraining via Amortization. - Haotian Sun; Yitong Li; Yuchen Zhuang; Niao He; Hanjun Dai; Bo Dai 001 (CoRR, 2025)

PoLAR: Polar-Decomposed Low-Rank Adapter Representation. - Kai Lion; Liang Zhang; Bingcong Li; Niao He (CoRR, 2025)

Zeroth-Order Optimization Finds Flat Minima. - Liang Zhang; Bingcong Li; Kiran Koshy Thekumparampil; Sewoong Oh; Michael Muehlebach; Niao He (CoRR, 2025)

Provable Maximum Entropy Manifold Exploration via Diffusion Models. - Riccardo De Santi; Marin Vlastelica; Ya-Ping Hsieh; Zebang Shen; Niao He; Andreas Krause 001 (CoRR, 2025)