23 citations · 119 across the 32 of their papers we have counts for
6 papers · 1 filter
Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds
Feihu Huang, Shangqian Gao
In the paper, we study a class of useful minimax problems on Riemanian manifolds and propose a class of effective Riemanian gradient-based methods to solve these minimax problems.…
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
Feihu Huang, Songcan Chen, Heng Huang
In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential e…
Accelerated Stochastic Gradient-free and Projection-free Methods
Feihu Huang, Lue Tao, Songcan Chen
In the paper, we propose a class of accelerated stochastic gradient-free and projection-free (a.k.a., zeroth-order Frank-Wolfe) methods to solve the constrained stochastic and fini…
Momentum-Based Policy Gradient Methods
Feihu Huang, Shangqian Gao, Jian Pei +1
In the paper, we propose a class of efficient momentum-based policy gradient methods for the model-free reinforcement learning, which use adaptive learning rates and do not require…
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
Feihu Huang, Shangqian Gao, Jian Pei +1
In the paper, we propose a class of accelerated zeroth-order and first-order momentum methods for both nonconvex mini-optimization and minimax-optimization. Specifically, we propos…
Faster Stochastic Quasi-Newton Methods
Qingsong Zhang, Feihu Huang, Cheng Deng +1
Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machin…