4 papers · 1 filter
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…
Stochastic Primal-Dual Q-Learning
Narim Jeong, Donghwan Lee, Niao He
In this work, we present a new model-free and off-policy reinforcement learning (RL) algorithm, that is capable of finding a near-optimal policy with state-action observations from…
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 …
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
Yifan Hu, Siqi Zhang, Xin Chen +1
Conditional stochastic optimization covers a variety of applications ranging from invariant learning and causal inference to meta-learning. However, constructing unbiased gradient…