A Hessian-aware stochastic differential equation for modelling SGD

Xiang Li, Zebang Shen, Liang Zhang, Niao He · Mathematical Programming, 2026

A Convex Framework for Confounding Robust Inference

Kei Ishikawa, Niao He, Takafumi Kanamori · Journal of Machine Learning Research, 2026

Optical Computing with Spectrally Multiplexed Features in Complex Media

Xue Dong, Kai Lion, Fei Xia, YoonSeok Baek, Ziao Wang, Niao He, Sylvain Gigan · Optica, 2026

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing

Yudong Wei, Liang Zhang, Bingcong Li, Niao He · ICLR, 2026

Landing with the Score: Riemannian Optimization through Denoising

Andrey Kharitenko, Zebang Shen, Riccardo De Santi, Niao He, Florian Doerfler · ICLR, 2026

A Schrodinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control

Louis Claeys, Artur Goldman, Zebang Shen, Niao He · ICLR, 2026

When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis

Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He · ICLR, 2026 (Oral at the NeurIPS DynaFront Workshop)

Manifold Generalization Provably Proceeds Memorization in Diffusion Models

Zebang Shen, Ya-Ping Hsieh, Niao He · ICLR Workshop on Deep Generative Models, 2026 (Outstanding Best Paper)

Zeroth-Order Optimization at the Edge of Stability

Minhak Song, Liang Zhang, Bingcong Li, Niao He, Michael Muehlebach, Sewoong Oh · ICML, 2026 (Oral at the ICLR Workshop on Scientific Methods for Understanding Deep Learning)

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference

Hao Ma, Melis Ilayda Bal, Liang Zhang, Bingcong Li, Niao He, Melanie Zeilinger, Michael Muehlebach · ICML, 2026

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets

Saeed Masiha, Zebang Shen, Negar Kiyavash, Niao He · NeurIPS, 2026

Support Before Frequency in Discrete Diffusion

Adrian Müller, Antoine Gonon, Zebang Shen, Ya-Ping Hsieh, Niao He · NeurIPS, 2026

Global Optimality for Constrained Exploration via Penalty Regularization

Florian Wolf, Ilyas Fatkhullin, Niao He · NeurIPS, 2026

ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling

Yilang Zhang, Bingcong Li, Niao He, Georgios B. Giannakis · NeurIPS, 2026

Minima Selection in Stochastic Optimization: A Long-Time, Small-Stepsize Perspective

Xiang Li, Zebang Shen, Ya-Ping Hsieh, Niao He · Preprint, 2026

PoLAR: Polar-Decomposed Low-Rank Adapter Representation

Kai Lion, Liang Zhang, Bingcong Li, Niao He · NeurIPS, 2025

Zeroth-Order Optimization Finds Flat Minima

Liang Zhang, Bingcong Li, Kiran Thekumparampil, Sewoong Oh, Michael Muehlebach, Niao He · NeurIPS, 2025

AmorLIP: Efficient Language-Image Pretraining via Amortization

Haotian Sun, Yitong Li, Yuchen Zhuang, Niao He, Hanjun Dai, Bo Dai · 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 · NeurIPS, 2025 (Oral at the ICML GenBio Workshop)

Scalable Neural Incentive Design with Parameterized Mean-Field Approximation

Nathan Corecco, Batuhan Yardim, Vinzenz Thoma, Zebang Shen, Niao He · NeurIPS, 2025

Natural Gradient VI in Non-Conjugate Models

Fangyuan Sun, Ilyas Fatkhullin, Niao He · NeurIPS, 2025

A Variational Inequality Approach to Independent Learning in Static Mean-Field Games

Batuhan Yardim, Semih Cayci, Niao He · ACM/IMS Journal of Data Science, 2025

Primal Methods for Variational Inequality Problems with Functional Constraints

Liang Zhang, Niao He, Michael Muehlebach · Mathematical Programming, 2025

Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence

Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He · SIAM Journal on Control and Optimization, 2025

Stochastic Optimization under Hidden Convexity

Ilyas Fatkhullin, Niao He, Yifan Hu · SIAM Journal on Optimization, 2025

EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtárik · Journal of Machine Learning Research, 2025

On the Crucial Role of Initialization for Matrix Factorization

Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He · ICLR, 2025

Learning to Steer Markovian Agents under Model Uncertainty

Jiawei Huang, Vinzenz Thoma, Zebang Shen, Heinrich H Nax, Niao He · ICLR, 2025

From Gradient Clipping to Normalization for Heavy Tailed SGD

Florian Hubler, Ilyas Fatkhullin, Niao He · AISTATS, 2025

Steering No-Regret Agents in MFGs under Model Uncertainty

Leo Widmer, Jiawei Huang, Niao He · AISTATS, 2025

Efficiently Escaping Saddle Points for Policy Optimization

Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser · UAI, 2025

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning

Batuhan Yardim, Niao He · L4DC, 2025 (Best Paper Finalist)

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective

Jiawei Huang, Bingcong Li, Christoph Dann, Niao He · ICML, 2025

Policy Design in Long-Run Welfare Dynamics

Jiduan Wu, Rediet Abebe, Moritz Hardt, Ana-Andreea Stoica · ICLR, 2025

Provable Maximum Entropy Manifold Exploration via Diffusion Models

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause · ICML, 2025

Safe-EF: Error Feedback for Nonsmooth Constrained Optimization

Rustem Islamov, Yarden As, Ilyas Fatkhullin · ICML, 2025

Best of Both Worlds: Regret Minimization versus Minimax Play

Adrian Müller, Jon Schneider, Stratis Skoulakis, Luca Viano, Volkan Cevher · ICML, 2025

Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management

Xin Chen, Niao He, Yifan Hu, Zikun Ye · Operations Research, 2024

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

Semih Cayci, Niao He, R Srikant · SIAM Journal on Optimization, 2024

Finite-Time Analysis of Natural Actor-Critic for POMDPs

Semih Cayci, Niao He, R Srikant · SIAM Journal on Mathematics of Data Science, 2024

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

Semih Cayci, Niao He, R. Srikant · Transactions on Machine Learning Research, 2024

Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems

Bingcong Li, 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 · NeurIPS, 2024

DPZero: Private Fine-Tuning of Language Models without Backpropagation

Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He · ICML, 2024

Truly No-Regret Learning in Constrained MDPs

Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He · ICML, 2024

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

Jiawei Huang, Niao He, and Andreas Krause · ICML, 2024

When is Mean-Field Reinforcement Learning Tractable and Relevant?

Batuhan Yardim, Artur Goldman and Niao He · AAMAS, 2024

Provably Learning Nash Policies in Constrained Markov Potential Games

Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause · AAMAS, 2024

Automated Design of Affine Maximizer Mechanisms in Dynamic Settings

Michael Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen Marcus McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken · AAAI, 2024

Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players

Pragnya Alatur, Anas Barakat, and Niao He · CDC, 2024

Parameter-Agnostic Optimization under Relaxed Smoothness

Florian Hübler, Junchi Yang, Xiang Li, Niao He · AISTATS, 2024

On the Statistical Efficiency of Mean Field RL with General Function Approximation

Jiawei Huang, Batuhan Yardim, and Niao He · AISTATS, 2024

Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization

Siqi Zhang, Yifan Hu, Liang Zhang, Niao He · AISTATS, 2024

Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence

Ilyas Fatkhullin and Niao He · AISTATS, 2024

Independent Learning in Constrained Markov Potential Games

Philip Jordan, Anas Barakat, and Niao He · AISTATS, 2024

Momentum-Based Policy Gradient with Second-Order Information

Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran · Transactions on Machine Learning Research, 2024

Provably Convergent Policy Optimization via Metric-aware Trust Region Methods

Jun Song, Niao He, Lijun Ding, Chaoyue Zhao · Transactions on Machine Learning Research, 2023

Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation

Cayci, Semih, Siddhartha Satpathi, Niao He, and R. Srikant · IEEE Transactions on Automatic Control, 2023

A Discrete-time Switching System Analysis of Q-learning

Donghwan Lee, Jianghai Hu, and Niao He · SIAM Journal on Control and Optimization, 2023

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization

Liang Zhang, Junchi Yang, Amin Karbasi, Niao He · NeurIPS, 2023 (Spotlight)

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

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

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

Xiang Li, Junchi Yang, Niao He · ICLR, 2023

Policy mirror ascent for efficient and independent learning in mean field games

Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He · ICML, 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

Kernel Conditional Moment Constraints for Confounding Robust Inference

Kei Ishikawa and Niao He · AISTATS, 2023

Learning to Optimize for Stochastic Dominance Constraints

Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai · AISTATS, 2023

Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality

Ilyas Fatkhullin, Jalal Etesami, Niao He, Negar Kiyavash · NeurIPS, 2022

Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization

Liang Zhang, Kiran Koshy 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

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

Saeed Masiha, Saber Salehkaleybar, Niao He, Negar Kiyavash, Patrick Thiran · NeurIPS, 2022

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

Ahmet Alacaoglu, Luca Viano, Niao He, Volkan Cevher · ICML, 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 Koshy Thekumparampil, Niao He, Sewoong Oh · AISTATS, 2022 (Oral)

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, and Niao He · UAI, 2021

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

Cayci, Semih, Siddhartha Satpathi, Niao He, and R. Srikant · ICML Workshop on Overparametrization, 2021

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

Yingxiang Yang, Negar Kiyavash, Le Song, and Niao He · NeurIPS, 2020 (Spotlight)

A Catalyst Framework for Minimax Optimization

Junchi Yang, Siqi Zhang, Negar Kiyavash, and Niao He · NeurIPS, 2020

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

Donghwan Lee and Niao He · NeurIPS, 2020

Provably-Efficient Double Q-Learning

Wentao Weng, Harsh Gupta, Niao He, Lei Ying, and R Srikant · NeurIPS, 2020

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

Junchi Yang, Negar Kiyavash, and Niao He · NeurIPS, 2020

Biased Stochastic Gradient Descent for Conditional Stochastic Optimization

Yifan Hu, Siqi Zhang, Xin Chen, and Niao He · NeurIPS, 2020

Periodic Q-Learning

Donghwan Lee and Niao He · L4DC, 2020

Quadratic Decomposable Submodular Function Minimization: Theory and Practice

Pan Li, Niao He, Olgica Milenkovic · Journal of Machine Learning Research, 2020

Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents

Donghwan Lee, Niao He, Parameswaran Kamalaruban, Volkan Cevher · IEEE Signal Processing Magazine, 2020

Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization

Yifan Hu, Xin Chen, and Niao He · SIAM Journal on Optimization, 2020

Point Process Estimation with Mirror Prox Algorithms

Niao He, Zaid Harchaoui, Yichen Wang, and Le Song · Applied Mathematics and Optimization, 2019

Learning Positive Functions with Pseudo Mirror Descent

Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, and Niao He · NeurIPS, 2019 (Spotlight)

Exponential Family Estimation via Adversarial Dynamics Embedding

Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, and Dale Schuurmans · NeurIPS, 2019

Target-Based Temporal Difference Learning

Donghwan Lee, Niao He · ICML, 2019

Optimization and Learning Algorithms for Stochastic and Adversarial Power Control

Harsh Gupta, Niao He, and R. Srikant · WiOpt, 2019

Kernel Exponential Family Estimation via Doubly Dual Embedding

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He · AISTATS, 2019

Dynamic Programming for Stochastic Control Systems with Jointly Discrete and Continuous State-Spaces

Donghwan Lee, Niao He, Jianghai Hu · ACC, 2019

Stochastic Primal-Dual Q-Learning Algorithms for Discounted MDPs

Donghwan Lee, Niao He · ACC, 2019

Coupled Variational Bayes via Optimization Embedding

Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song · NIPS, 2018

Quadratic Decomposable Submodular Function Minimization

Pan Li, Niao He, Olgica Milenkovic · NIPS, 2018

Predictive Approximate Bayesian Computation via Saddle Points

Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He · NIPS, 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 · ICML, 2018

Boosting The Actor With Dual Critic

Bo Dai, Albert Shaw, Niao He, Lihong Li, and Le Song · ICLR, 2018

Online Learning for Multivariate Hawkes Processes

Yingxiang Yang, Jalal Etsami, Niao He, and Negar Kiyavash · NIPS, 2017

Smoothed Dual Embedding Control

Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song · NIPS Deep Reinforcement Learning Symposium, 2017

Stochastic Generative Hashing

Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song · ICML, 2017

Learning from Conditional Distributions via Dual Kernel Embeddings

Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song · AISTATS, 2017

Provable Bayesian Inference via Particle Mirror Descent

Bo Dai, Niao He, Hanjun Dai, and Le Song · AISTATS, 2016

Saddle Point Techniques in Convex Composite and Error-in-Measurement Optimization

Niao He · Georgia Institute of Technology, 2015 (PhD thesis)

Mirror Prox Algorithm for Multi-Term Composite Minimization and Semi-Separable Problems

Niao He, Anatoli Juditsky, and Arkadi Nemirovski · Computational Optimization and Applications, 2015

Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization

Niao He and Zaid Harchaoui · NIPS, 2015

Time-sensitive Recommendation From Recurrent User Activities

Nan Du, Yichen Wang, Niao He, and Le Song · NIPS, 2015

Stochastic Semi-Proximal Mirror Prox

Niao He and Zaid Harchaoui · NIPS Workshop on Optimization for Machine Learning, 2015

Scalable Kernel Methods via Doubly Stochastic Gradients

Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina Balcan, and Le Song · NIPS, 2014

Stochastic Alternating Direction Method of Multipliers

Hua Ouyang, Niao He, Long Tran, and Alexander Gray · ICML, 2013