19 citations · 40 across the 18 of their papers we have counts for
7 papers · 1 filter
Enhanced Bilevel Optimization via Bregman Distance
Feihu Huang, Junyi Li, Shangqian Gao +1
Bilevel optimization has been recently used in many machine learning problems such as hyperparameter optimization, policy optimization, and meta learning. Although many bilevel opt…
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization
Feihu Huang, Xidong Wu, Zhengmian Hu
In the paper, we propose a class of faster adaptive Gradient Descent Ascent (GDA) methods for solving the nonconvex-strongly-concave minimax problems by using the unified adaptive…
Bregman Gradient Policy Optimization
Feihu Huang, Shangqian Gao, Heng Huang
In the paper, we design a novel Bregman gradient policy optimization framework for reinforcement learning based on Bregman divergences and momentum techniques. Specifically, we pro…
BiAdam: Fast Adaptive Bilevel Optimization Methods
Feihu Huang, Junyi Li, Shangqian Gao
Bilevel optimization recently has attracted increased interest in machine learning due to its many applications such as hyper-parameter optimization and meta learning. Although man…
Compositional federated learning: Applications in distributionally robust averaging and meta learning
Feihu Huang, Junyi Li
In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which fr…
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
Feihu Huang, Junyi Li, Heng Huang
Adaptive gradient methods have shown excellent performances for solving many machine learning problems. Although multiple adaptive gradient methods were recently studied, they main…