ML Anthology

He, Niao

87 publications

NeurIPS 2025 AmorLIP: Efficient Language-Image Pretraining via Amortization Haotian Sun, Yitong Li, Yuchen Zhuang, Niao He, Hanjun Dai, Bo Dai
ICML 2025 Can RLHF Be More Efficient with Imperfect Reward Models? a Policy Coverage Perspective Jiawei Huang, Bingcong Li, Christoph Dann, Niao He
ICLRW 2025 Can RLHF Be More Efficient with Imperfect Reward Models? a Policy Coverage Perspective Jiawei Huang, Bingcong Li, Christoph Dann, Niao He
UAI 2025 Efficiently Escaping Saddle Points for Policy Optimization Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
L4DC 2025 Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning Batuhan Yardim, Niao He
NeurIPS 2025 Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
AISTATS 2025 From Gradient Clipping to Normalization for Heavy Tailed SGD Florian Hübler, Ilyas Fatkhullin, Niao He
ICLR 2025 Learning to Steer Markovian Agents Under Model Uncertainty Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He
NeurIPS 2025 Natural Gradient VI: Guarantees for Non-Conjugate Models Fangyuan Sun, Ilyas Fatkhullin, Niao He
ICLR 2025 On the Crucial Role of Initialization for Matrix Factorization Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He
NeurIPS 2025 PoLAR: Polar-Decomposed Low-Rank Adapter Representation Kai Lion, Liang Zhang, Bingcong Li, Niao He
ICML 2025 Provable Maximum Entropy Manifold Exploration via Diffusion Models Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
ICLRW 2025 Provable Maximum Entropy Manifold Exploration via Diffusion Models Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
NeurIPS 2025 Scalable Neural Incentive Design with Parameterized Mean-Field Approximation Nathan Corecco, Batuhan Yardim, Vinzenz Thoma, Zebang Shen, Niao He
AISTATS 2025 Steering No-Regret Agents in MFGs Under Model Uncertainty Leo Widmer, Jiawei Huang, Niao He
NeurIPS 2025 Zeroth-Order Optimization Finds Flat Minima Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Michael Muehlebach, Niao He
ICMLW 2024 A Hessian-Aware Stochastic Differential Equation for Modelling SGD Xiang Li, Zebang Shen, Liang Zhang, Niao He
NeurIPS 2024 Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes Yan Huang, Xiang Li, Yipeng Shen, Niao He, Jinming Xu
AAAI 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
ICML 2024 DPZero: Private Fine-Tuning of Language Models Without Backpropagation Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
ICMLW 2024 Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning Batuhan Yardim, Niao He
TMLR 2024 Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm Semih Cayci, Niao He, R. Srikant
NeurIPSW 2024 From Gradient Clipping to Normalization for Heavy Tailed SGD Florian Hübler, Ilyas Fatkhullin, Niao He
AISTATS 2024 Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
NeurIPS 2024 Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems Bingcong Li, Liang Zhang, Niao He
ICMLW 2024 Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems Bingcong Li, Liang Zhang, Niao He
AISTATS 2024 Independent Learning in Constrained Markov Potential Games Philip Jordan, Anas Barakat, Niao He
ICMLW 2024 Learning to Steer Markovian Agents Under Model Uncertainty Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H. Nax, Niao He
ICML 2024 Model-Based RL for Mean-Field Games Is Not Statistically Harder than Single-Agent RL Jiawei Huang, Niao He, Andreas Krause
TMLR 2024 Momentum-Based Policy Gradient with Second-Order Information Saber Salehkaleybar, Mohammadsadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran
NeurIPSW 2024 On the Crucial Role of Initialization for Matrix Factorization Bingcong Li, Liang Zhang, Aryan Mokhtari, 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 Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence Ilyas Fatkhullin, Niao He
ICML 2024 Truly No-Regret Learning in Constrained MDPs Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
ICMLW 2024 Truly No-Regret Learning in Constrained MDPs Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
ICMLW 2024 When Is Mean-Field Reinforcement Learning Tractable and Relevant? Batuhan Yardim, Artur Goldman, Niao He
NeurIPSW 2023 DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
AISTATS 2023 Kernel Conditional Moment Constraints for Confounding Robust Inference Kei Ishikawa, Niao He
AISTATS 2023 Learning to Optimize with Stochastic Dominance Constraints Hanjun Dai, Yuan Xue, Niao He, Yixin Wang, Na Li, Dale Schuurmans, Bo Dai
NeurIPS 2023 On Imitation in Mean-Field Games Giorgia Ramponi, Pavel Kolev, Olivier Pietquin, Niao He, Mathieu Lauriere, Matthieu Geist
NeurIPS 2023 Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization Liang Zhang, Junchi Yang, Amin Karbasi, Niao He
NeurIPSW 2023 Parameter-Agnostic Optimization Under Relaxed Smoothness Florian Hübler, Junchi Yang, Xiang Li, Niao He
ICML 2023 Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He
TMLR 2023 Provably Convergent Policy Optimization via Metric-Aware Trust Region Methods Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
ICML 2023 Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space Anas Barakat, Ilyas Fatkhullin, Niao He
NeurIPS 2023 Robust Knowledge Transfer in Tiered Reinforcement Learning Jiawei Huang, Niao He
NeurIPSW 2023 Stochastic Optimization Under Hidden Convexity Ilyas Fatkhullin, Niao He, Yifan Hu
ICML 2023 Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-Non-Degenerate Policies Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He
ICLR 2023 TiAda: A Time-Scale Adaptive Algorithm for Nonconvex Minimax Optimization Xiang Li, Junchi Yang, Niao He
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
AISTATS 2022 Faster Single-Loop Algorithms for Minimax Optimization Without Strong Concavity Junchi Yang, Antonio Orvieto, Aurelien Lucchi, Niao He
AISTATS 2022 Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization Kiran K. Thekumparampil, Niao He, Sewoong Oh
ICML 2022 A Natural Actor-Critic Framework for Zero-Sum Markov Games Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher
NeurIPS 2022 Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization Liang Zhang, Kiran K Thekumparampil, Sewoong Oh, Niao He
NeurIPS 2022 Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization Junchi Yang, Xiang Li, Niao He
NeurIPS 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
NeurIPSW 2022 TiAda: A Time-Scale Adaptive Algorithm for Nonconvex Minimax Optimization Xiang Li, Junchi Yang, Niao He
NeurIPSW 2022 Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
NeurIPS 2021 On the Bias-Variance-Cost Tradeoff of Stochastic Optimization Yifan Hu, Xin Chen, Niao He
UAI 2021 The Complexity of Nonconvex-Strongly-Concave Minimax Optimization Siqi Zhang, Junchi Yang, Cristóbal Guzmán, Negar Kiyavash, Niao He
NeurIPS 2020 A Catalyst Framework for Minimax Optimization Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He
NeurIPS 2020 A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms Donghwan Lee, 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 Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang, Negar Kiyavash, Niao He
L4DC 2020 Periodic Q-Learning Donghwan Lee, Niao He
JMLR 2020 Quadratic Decomposable Submodular Function Minimization: Theory and Practice Pan Li, Niao He, Olgica Milenkovic
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 The Mean-Squared Error of Double Q-Learning Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant
NeurIPS 2019 Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
AISTATS 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
NeurIPS 2019 Learning Positive Functions with Pseudo Mirror Descent Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He
ICML 2019 Target-Based Temporal-Difference Learning Donghwan Lee, Niao He
ICLR 2018 Boosting the Actor with Dual Critic Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song
NeurIPS 2018 Coupled Variational Bayes via Optimization Embedding Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS 2018 Predictive Approximate Bayesian Computation via Saddle Points Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
NeurIPS 2018 Quadratic Decomposable Submodular Function Minimization Pan Li, Niao He, Olgica Milenkovic
ICML 2018 SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
AISTATS 2017 Learning from Conditional Distributions via Dual Embeddings Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song
NeurIPS 2017 Online Learning for Multivariate Hawkes Processes Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
ICML 2017 Stochastic Generative Hashing Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song
AISTATS 2016 Provable Bayesian Inference via Particle Mirror Descent Bo Dai, Niao He, Hanjun Dai, Le Song
NeurIPS 2015 Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization Niao He, Zaid Harchaoui
NeurIPS 2015 Time-Sensitive Recommendation from Recurrent User Activities Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song
NeurIPS 2014 Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F Balcan, Le Song
ICML 2013 Stochastic Alternating Direction Method of Multipliers Hua Ouyang, Niao He, Long Tran, Alexander Gray