19 citations · 34 across the 8 of their papers we have counts for
11 papers
Fast Adaptive Federated Bilevel Optimization
Feihu Huang
Bilevel optimization is a popular hierarchical model in machine learning, and has been widely applied to many machine learning tasks such as meta learning, hyperparameter learning…
Communication-Efficient Adam-Type Algorithms for Distributed Data Mining
Wenhan Xian, Feihu Huang, Heng Huang
Distributed data mining is an emerging research topic to effectively and efficiently address hard data mining tasks using big data, which are partitioned and computed on different…
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction
Junyi Li, Feihu Huang, Heng Huang
Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms and has been applied to many machine learning tasks such as data cleaning, few-s…
A New Framework for Variance-Reduced Hamiltonian Monte Carlo
Zhengmian Hu, Feihu Huang, Heng Huang
We propose a new framework of variance-reduced Hamiltonian Monte Carlo (HMC) methods for sampling from an -smooth and -strongly log-concave distribution, based on a unified f…
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…