137 citations · 656 across the 54 of their papers we have counts for
12 papers · 1 filter
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Batuhan Yardim, Semih Cayci, Matthieu Geist +1
Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous -player games. However, limiting applicability, exist…
Sharp Analysis of Stochastic Optimization under Global Kurdyka-Łojasiewicz Inequality
Ilyas Fatkhullin, Jalal Etesami, Niao He +1
We study the complexity of finding the global solution to stochastic nonconvex optimization when the objective function satisfies global Kurdyka-Lojasiewicz (KL) inequality and the…
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
Xiang Li, Junchi Yang, Niao He
Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimiz…
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm
Semih Cayci, Niao He, R. Srikant
Natural actor-critic (NAC) and its variants, equipped with the representation power of neural networks, have demonstrated impressive empirical success in solving Markov decision pr…
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization
Junchi Yang, Xiang Li, Niao He
Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization owing to their parameter-agnostic ability -- requiring no a priori knowledge about problem-spe…
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
Liang Zhang, Kiran Koshy Thekumparampil, Sewoong Oh +1
We study differentially private (DP) algorithms for smooth stochastic minimax optimization, with stochastic minimization as a byproduct. The holy grail of these settings is to guar…
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
Siqi Zhang, Yifan Hu, Liang Zhang +1
This paper takes an initial step to systematically investigate the generalization bounds of algorithms for solving nonconvex-(strongly)-concave (NC-SC/NC-C) stochastic minimax opti…
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function
Saeed Masiha, Saber Salehkaleybar, Niao He +2
We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with which holds in a wide range o…
Momentum-Based Policy Gradient with Second-Order Information
Saber Salehkaleybar, Sadegh Khorasani, Negar Kiyavash +2
Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleratio…
Efficient Algorithms for A Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management
Xin Chen, Niao He, Yifan Hu +1
We study a class of stochastic nonconvex optimization in the form of , i.e., is a composition of a convex function and…
Finite-Time Analysis of Natural Actor-Critic for POMDPs
Semih Cayci, Niao He, R. Srikant
We consider the reinforcement learning problem for partially observed Markov decision processes (POMDPs) with large or even countably infinite state spaces, where the controller ha…
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
We study the bilinearly coupled minimax problem: , where and are both strongly convex smooth functions and admit first-order gra…