Niao He - RankMe

173 papers A* 38A 18B 1C 2Journal 104Unranked 10

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control. CoRR Louis Claeys, Artur Goldman, Zebang Shen, Niao He
2026	J	`jnl`	ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling. CoRR Yilang Zhang, Bingcong Li, Niao He, Georgios B. Giannakis
2026	J	`jnl`	Manifold Generalization Provably Proceeds Memorization in Diffusion Models. CoRR Zebang Shen, Ya-Ping Hsieh, Niao He
2026	J	`jnl`	SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference. CoRR Hao Ma, Melis Ilayda Bal, Liang Zhang, Bingcong Li, Niao He, Melanie Nicole Zeilinger, Michael Muehlebach
2025	J	`jnl`	A Variational Inequality Approach to Independent Learning in Static Mean-Field Games. CoRR Batuhan Yardim, Semih Cayci, Niao He
2025	J	`jnl`	AmorLIP: Efficient Language-Image Pretraining via Amortization. CoRR Haotian Sun, Yitong Li, Yuchen Zhuang, Niao He, Hanjun Dai, Bo Dai
2025	A*	`conf`	Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective. ICML Jiawei Huang, Bingcong Li, Christoph Dann, Niao He
2025	J	`jnl`	Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective. CoRR Jiawei Huang, Bingcong Li, Christoph Dann, Niao He
2025	J	`jnl`	Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management. Oper. Res. Xin Chen, Niao He, Yifan Hu, Zikun Ye
2025	A	`conf`	Efficiently Escaping Saddle Points for Policy Optimization. UAI Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
2025	—	`conf`	Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning. L4DC Batuhan Yardim, Niao He
2025	J	`jnl`	Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning. CoRR Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
2025	A	`conf`	From Gradient Clipping to Normalization for Heavy Tailed SGD. AISTATS Florian Hübler, Ilyas Fatkhullin, Niao He
2025	J	`jnl`	Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity. CoRR Ilyas Fatkhullin, Niao He, Guanghui Lan, Florian Wolf
2025	J	`jnl`	Landing with the Score: Riemannian Optimization through Denoising. CoRR Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Dörfler
2025	J	`jnl`	Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence. SIAM J. Control. Optim. Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
2025	A*	`conf`	Learning to Steer Markovian Agents under Model Uncertainty. ICLR Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He
2025	J	`jnl`	Natural Gradient VI: Guarantees for Non-Conjugate Models. CoRR Fangyuan Sun, Ilyas Fatkhullin, Niao He
2025	J	`jnl`	On the Benefits of Weight Normalization for Overparameterized Matrix Sensing. CoRR Yudong Wei, Liang Zhang, Bingcong Li, Niao He
2025	A*	`conf`	On the Crucial Role of Initialization for Matrix Factorization. ICLR Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He
2025	J	`jnl`	PoLAR: Polar-Decomposed Low-Rank Adapter Representation. CoRR Kai Lion, Liang Zhang, Bingcong Li, Niao He
2025	A*	`conf`	Provable Maximum Entropy Manifold Exploration via Diffusion Models. ICML Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
2025	J	`jnl`	Provable Maximum Entropy Manifold Exploration via Diffusion Models. CoRR Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
2025	J	`jnl`	Scalable Neural Incentive Design with Parameterized Mean-Field Approximation. CoRR Nathan Corecco, Batuhan Yardim, Vinzenz Thoma, Zebang Shen, Niao He
2025	A	`conf`	Steering No-Regret Agents in MFGs under Model Uncertainty. AISTATS Leo Widmer, Jiawei Huang, Niao He
2025	J	`jnl`	Steering No-Regret Agents in MFGs under Model Uncertainty. CoRR Leo Widmer, Jiawei Huang, Niao He
2025	J	`jnl`	Stochastic Optimization under Hidden Convexity. SIAM J. Optim. Ilyas Fatkhullin, Niao He, Yifan Hu
2025	J	`jnl`	When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis. CoRR Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He
2025	J	`jnl`	Zeroth-Order Optimization Finds Flat Minima. CoRR Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Michael Muehlebach, Niao He
2024	J	`jnl`	A Hessian-Aware Stochastic Differential Equation for Modelling SGD. CoRR Xiang Li, Zebang Shen, Liang Zhang, Niao He
2024	A*	`conf`	Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes. NeurIPS Yan Huang, Xiang Li, Yipeng Shen, Niao He, Jinming Xu
2024	J	`jnl`	Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes. CoRR Yan Huang, Xiang Li, Yipeng Shen, Niao He, Jinming Xu
2024	A*	`conf`	Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. AAAI Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken
2024	J	`jnl`	Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. CoRR Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken
2024	J	`jnl`	Complexity of Minimizing Projected-Gradient-Dominated Functions with Stochastic First-order Oracles. CoRR Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran
2024	J	`jnl`	Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. SIAM J. Optim. Semih Cayci, Niao He, R. Srikant
2024	A*	`conf`	DPZero: Private Fine-Tuning of Language Models without Backpropagation. ICML Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
2024	J	`jnl`	Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning. CoRR Batuhan Yardim, Niao He
2024	J	`jnl`	Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. Trans. Mach. Learn. Res. Semih Cayci, Niao He, R. Srikant
2024	J	`jnl`	Finite-Time Analysis of Natural Actor-Critic for POMDPs. SIAM J. Math. Data Sci. Semih Cayci, Niao He, R. Srikant
2024	J	`jnl`	From Gradient Clipping to Normalization for Heavy Tailed SGD. CoRR Florian Hübler, Ilyas Fatkhullin, Niao He
2024	A	`conf`	Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization. AISTATS Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
2024	A*	`conf`	Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems. NeurIPS Bingcong Li, Liang Zhang, Niao He
2024	J	`jnl`	Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems. CoRR Bingcong Li, Liang Zhang, Niao He
2024	A	`conf`	Independent Learning in Constrained Markov Potential Games. AISTATS Philip Jordan, Anas Barakat, Niao He
2024	J	`jnl`	Independent Learning in Constrained Markov Potential Games. CoRR Philip Jordan, Anas Barakat, Niao He
2024	—	`conf`	Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. CDC Pragnya Alatur, Anas Barakat, Niao He
2024	J	`jnl`	Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. CoRR Pragnya Alatur, Anas Barakat, Niao He
2024	J	`jnl`	Learning to Steer Markovian Agents under Model Uncertainty. CoRR Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He
2024	A*	`conf`	Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. ICML Jiawei Huang, Niao He, Andreas Krause
2024	J	`jnl`	Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. CoRR Jiawei Huang, Niao He, Andreas Krause
2024	J	`jnl`	Momentum-Based Policy Gradient with Second-Order Information. Trans. Mach. Learn. Res. Saber Salehkaleybar, Mohammadsadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran
2024	J	`jnl`	Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles. CoRR Yifan Hu, Jie Wang, Xin Chen, Niao He
2024	J	`jnl`	On the Crucial Role of Initialization for Matrix Factorization. CoRR Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He
2024	A	`conf`	On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation. AISTATS Jiawei Huang, Batuhan Yardim, Niao He
2024	A	`conf`	Parameter-Agnostic Optimization under Relaxed Smoothness. AISTATS Florian Hübler, Junchi Yang, Xiang Li, Niao He
2024	J	`jnl`	Primal Methods for Variational Inequality Problems with Functional Constraints. CoRR Liang Zhang, Niao He, Michael Muehlebach
2024	A	`conf`	Provably Learning Nash Policies in Constrained Markov Potential Games. AAMAS Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause
2024	J	`jnl`	Stochastic Optimization under Hidden Convexity. CoRR Ilyas Fatkhullin, Niao He, Yifan Hu
2024	A	`conf`	Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. AISTATS Ilyas Fatkhullin, Niao He
2024	J	`jnl`	Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence. CoRR Ilyas Fatkhullin, Niao He
2024	A*	`conf`	Truly No-Regret Learning in Constrained MDPs. ICML Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
2024	J	`jnl`	Truly No-Regret Learning in Constrained MDPs. CoRR Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
2024	A	`conf`	When is Mean-Field Reinforcement Learning Tractable and Relevant? AAMAS Batuhan Yardim, Artur Goldman, Niao He
2024	J	`jnl`	When is Mean-Field Reinforcement Learning Tractable and Relevant? CoRR Batuhan Yardim, Artur Goldman, Niao He
2023	J	`jnl`	A Convex Framework for Confounding Robust Inference. CoRR Kei Ishikawa, Niao He, Takafumi Kanamori
2023	J	`jnl`	A Discrete-Time Switching System Analysis of Q-Learning. SIAM J. Control. Optim. Donghwan Lee, Jianghai Hu, Niao He
2023	J	`jnl`	Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes. CoRR Adrian Müller, Pragnya Alatur, Giorgia Ramponi, Niao He
2023	J	`jnl`	DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. CoRR Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
2023	J	`jnl`	Efficiently Escaping Saddle Points for Non-Convex Policy Optimization. CoRR Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
2023	A	`conf`	Kernel Conditional Moment Constraints for Confounding Robust Inference. AISTATS Kei Ishikawa, Niao He
2023	J	`jnl`	Kernel Conditional Moment Constraints for Confounding Robust Inference. CoRR Kei Ishikawa, Niao He
2023	J	`jnl`	Learning Best Response Policies in Dynamic Auctions via Deep Reinforcement Learning. CoRR Vinzenz Thoma, Michael J. Curry, Niao He, Sven Seuken
2023	—	`conf`	Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CDC Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
2023	J	`jnl`	Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. CoRR Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
2023	A	`conf`	Learning to Optimize with Stochastic Dominance Constraints. AISTATS Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai
2023	A*	`conf`	On Imitation in Mean-field Games. NeurIPS Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist
2023	J	`jnl`	On Imitation in Mean-field Games. CoRR Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Laurière, Matthieu Geist
2023	J	`jnl`	On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation. CoRR Jiawei Huang, Batuhan Yardim, Niao He
2023	A*	`conf`	Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. NeurIPS Liang Zhang, Junchi Yang, Amin Karbasi, Niao He
2023	J	`jnl`	Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization. CoRR Liang Zhang, Junchi Yang, Amin Karbasi, Niao He
2023	J	`jnl`	Parameter-Agnostic Optimization under Relaxed Smoothness. CoRR Florian Hübler, Junchi Yang, Xiang Li, Niao He
2023	A*	`conf`	Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. ICML Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He
2023	J	`jnl`	Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. CoRR Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
2023	J	`jnl`	Provably Convergent Policy Optimization via Metric-aware Trust Region Methods. Trans. Mach. Learn. Res. Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
2023	J	`jnl`	Provably Learning Nash Policies in Constrained Markov Potential Games. CoRR Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause
2023	A*	`conf`	Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML Anas Barakat, Ilyas Fatkhullin, Niao He
2023	J	`jnl`	Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. CoRR Anas Barakat, Ilyas Fatkhullin, Niao He
2023	A*	`conf`	Robust Knowledge Transfer in Tiered Reinforcement Learning. NeurIPS Jiawei Huang, Niao He
2023	J	`jnl`	Robust Knowledge Transfer in Tiered Reinforcement Learning. CoRR Jiawei Huang, Niao He
2023	J	`jnl`	Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation. IEEE Trans. Autom. Control. Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant
2023	A*	`conf`	Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He
2023	J	`jnl`	Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. CoRR Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He
2023	A*	`conf`	TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. ICLR Xiang Li, Junchi Yang, Niao He
2023	A*	`conf`	Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. NeurIPS Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He
2023	J	`jnl`	Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods. CoRR Junchi Yang, Xiang Li, Ilyas Fatkhullin, Niao He
2022	A*	`conf`	A Natural Actor-Critic Framework for Zero-Sum Markov Games. ICML Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher
2022	J	`jnl`	Adaptive Momentum-Based Policy Gradient with Second-Order Information. CoRR Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran
2022	A*	`conf`	Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. NeurIPS Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
2022	J	`jnl`	Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization. CoRR Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
2022	A	`conf`	Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
2022	J	`jnl`	Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. CoRR Semih Cayci, Niao He, R. Srikant
2022	J	`jnl`	Learning to Control Partially Observed Systems with Finite Memory. CoRR Semih Cayci, Niao He, R. Srikant
2022	J	`jnl`	Learning to Optimize with Stochastic Dominance Constraints. CoRR Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai
2022	A	`conf`	Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. AISTATS Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
2022	J	`jnl`	Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization. CoRR Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
2022	A*	`conf`	Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. NeurIPS Junchi Yang, Xiang Li, Niao He
2022	J	`jnl`	Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization. CoRR Junchi Yang, Xiang Li, Niao He
2022	J	`jnl`	Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. CoRR Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He
2022	A*	`conf`	Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality. NeurIPS Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash
2022	A*	`conf`	Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions. NeurIPS Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran
2022	J	`jnl`	Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions. CoRR Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran
2022	J	`jnl`	TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. CoRR Xiang Li, Junchi Yang, Niao He
2022	J	`jnl`	Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems. CoRR Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
2021	J	`jnl`	Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He
2021	J	`jnl`	Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation. CoRR Semih Cayci, Niao He, R. Srikant
2021	A*	`conf`	On the Bias-Variance-Cost Tradeoff of Stochastic Optimization. NeurIPS Yifan Hu, Xin Chen, Niao He
2021	J	`jnl`	Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning. CoRR Semih Cayci, Siddhartha Satpathi, Niao He, R. Srikant
2021	J	`jnl`	Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction. CoRR Donghwan Lee, Niao He, Seungjae Lee, Panagiota Karava, Jianghai Hu
2021	J	`jnl`	The Complexity of Nonconvex-Strongly-Concave Minimax Optimization. CoRR Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He
2021	A	`conf`	The complexity of nonconvex-strongly-concave minimax optimization. UAI Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He
2020	A*	`conf`	A Catalyst Framework for Minimax Optimization. NeurIPS Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He
2020	A*	`conf`	A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms. NeurIPS Donghwan Lee, Niao He
2020	A*	`conf`	Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning. NeurIPS Yifan Hu, Siqi Zhang, Xin Chen, Niao He
2020	J	`jnl`	Biased Stochastic Gradient Descent for Conditional Stochastic Optimization. CoRR Yifan Hu, Siqi Zhang, Xin Chen, Niao He
2020	A*	`conf`	Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems. NeurIPS Junchi Yang, Negar Kiyavash, Niao He
2020	J	`jnl`	Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems. CoRR Junchi Yang, Negar Kiyavash, Niao He
2020	J	`jnl`	Optimization for Reinforcement Learning: From a single agent to cooperative agents. IEEE Signal Process. Mag. Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher
2020	—	`conf`	Periodic Q-Learning. L4DC Donghwan Lee, Niao He
2020	J	`jnl`	Periodic Q-Learning. CoRR Donghwan Lee, Niao He
2020	J	`jnl`	Provably-Efficient Double Q-Learning. CoRR Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
2020	J	`jnl`	Quadratic Decomposable Submodular Function Minimization: Theory and Practice. J. Mach. Learn. Res. Pan Li, Niao He, Olgica Milenkovic
2020	J	`jnl`	Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization. SIAM J. Optim. Yifan Hu, Xin Chen, Niao He
2020	A*	`conf`	The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models. NeurIPS Yingxiang Yang, Negar Kiyavash, Le Song, Niao He
2020	A*	`conf`	The Mean-Squared Error of Double Q-Learning. NeurIPS Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
2019	J	`jnl`	A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms. CoRR Donghwan Lee, Niao He
2019	C	`conf`	Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces. ACC Donghwan Lee, Niao He, Jianghai Hu
2019	A*	`conf`	Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
2019	J	`jnl`	Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
2019	A	`conf`	Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
2019	A*	`conf`	Learning Positive Functions with Pseudo Mirror Descent. NeurIPS Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He
2019	B	`conf`	Optimization and Learning Algorithms for Stochastic and Adversarial Power Control. WiOpt Harsh Gupta, Niao He, R. Srikant
2019	J	`jnl`	Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents. CoRR Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher
2019	J	`jnl`	Quadratic Decomposable Submodular Function Minimization: Theory and Practice. CoRR Pan Li, Niao He, Olgica Milenkovic
2019	C	`conf`	Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs. ACC Donghwan Lee, Niao He
2019	J	`jnl`	Target-Based Temporal Difference Learning. CoRR Donghwan Lee, Niao He
2019	A*	`conf`	Target-Based Temporal-Difference Learning. ICML Donghwan Lee, Niao He
2018	—	`conf`	Boosting the Actor with Dual Critic. ICLR (Poster) Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song
2018	A*	`conf`	Coupled Variational Bayes via Optimization Embedding. NeurIPS Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
2018	J	`jnl`	Kernel Exponential Family Estimation via Doubly Dual Embedding. CoRR Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
2018	A*	`conf`	Predictive Approximate Bayesian Computation via Saddle Points. NeurIPS Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
2018	A*	`conf`	Quadratic Decomposable Submodular Function Minimization. NeurIPS Pan Li, Niao He, Olgica Milenkovic
2018	J	`jnl`	Quadratic Decomposable Submodular Function Minimization. CoRR Pan Li, Niao He, Olgica Milenkovic
2018	A*	`conf`	SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. ICML Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
2017	J	`jnl`	Boosting the Actor with Dual Critic. CoRR Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song
2017	A	`conf`	Learning from Conditional Distributions via Dual Embeddings. AISTATS Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song
2017	—	`conf`	Online Learning for Multivariate Hawkes Processes. NIPS Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
2017	J	`jnl`	Smoothed Dual Embedding Control. CoRR Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song
2017	A*	`conf`	Stochastic Generative Hashing. ICML Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song
2017	J	`jnl`	Stochastic Generative Hashing. CoRR Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song
2016	J	`jnl`	Fast and Simple Optimization for Poisson Likelihood Models. CoRR Niao He, Zaïd Harchaoui, Yichen Wang, Le Song
2016	J	`jnl`	Learning from Conditional Distributions via Dual Kernel Embeddings. CoRR Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song
2016	A	`conf`	Provable Bayesian Inference via Particle Mirror Descent. AISTATS Bo Dai, Niao He, Hanjun Dai, Le Song
2015	J	`jnl`	Mirror Prox algorithm for multi-term composite minimization and semi-separable problems. Comput. Optim. Appl. Niao He, Anatoli B. Juditsky, Arkadi Nemirovski
2015	J	`jnl`	Scalable Bayesian Inference via Particle Mirror Descent. CoRR Bo Dai, Niao He, Hanjun Dai, Le Song
2015	—	`conf`	Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization. NIPS Niao He, Zaïd Harchaoui
2015	J	`jnl`	Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization. CoRR Niao He, Zaïd Harchaoui
2015	—	`conf`	Time-Sensitive Recommendation From Recurrent User Activities. NIPS Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song
2014	—	`conf`	Scalable Kernel Methods via Doubly Stochastic Gradients. NIPS Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song
2014	J	`jnl`	Scalable Kernel Methods via Doubly Stochastic Gradients. CoRR Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, Le Song
2013	J	`jnl`	Mirror Prox Algorithm for Multi-Term Composite Minimization and Alternating Directions. CoRR Niao He, Anatoli B. Juditsky, Arkadi Nemirovski
2013	—	`conf`	Stochastic Alternating Direction Method of Multipliers. ICML (1) Hua Ouyang, Niao He, Long Q. Tran, Alexander G. Gray
2012	J	`jnl`	Stochastic ADMM for Nonsmooth Optimization CoRR Hua Ouyang, Niao He, Alexander G. Gray