b'Feihu Huang'

research

∙ 04/21/2023

Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems

Minimax optimization plays an important role in many machine learning ta...

0 Feihu Huang, et al. ∙

research

∙ 03/07/2023

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization

In the paper, we study a class of nonconvex nonconcave minimax optimizat...

0 Feihu Huang, et al. ∙

research

∙ 03/07/2023

On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level

Bilevel optimization is a popular two-level hierarchical optimization, w...

0 Feihu Huang, et al. ∙

research

∙ 02/13/2023

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems

Bilevel Optimization has witnessed notable progress recently with new em...

0 Junyi Li, et al. ∙

research

∙ 02/13/2023

FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging

Federated learning (FL) is an emerging learning paradigm to tackle massi...

0 Junyi Li, et al. ∙

research

∙ 12/02/2022

Faster Adaptive Federated Learning

Federated learning has attracted increasing attention with the emergence...

0 Xidong Wu, et al. ∙

research

∙ 11/14/2022

Adaptive Federated Minimax Optimization with Lower complexities

Federated learning is a popular distributed and privacy-preserving machi...

0 Feihu Huang, et al. ∙

research

∙ 11/03/2022

Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization

Composition optimization recently appears in many machine learning appli...

0 Feihu Huang, et al. ∙

research

∙ 11/02/2022

Fast Adaptive Federated Bilevel Optimization

Bilevel optimization is a popular hierarchical model in machine learning...

0 Feihu Huang, et al. ∙

research

∙ 10/14/2022

Communication-Efficient Adam-Type Algorithms for Distributed Data Mining

Distributed data mining is an emerging research topic to effectively and...

0 Wenhan Xian, et al. ∙

research

∙ 07/12/2022

Split Time Series into Patches: Rethinking Long-term Series Forecasting with Dateformer

Time is one of the most significant characteristics of time-series, yet ...

0 Julong Young, et al. ∙

research

∙ 05/03/2022

Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction

Bilevel Optimization has witnessed notable progress recently with new em...

7 Junyi Li, et al. ∙

research

∙ 07/26/2021

Enhanced Bilevel Optimization via Bregman Distance

Bilevel optimization has been widely applied many machine learning probl...

0 Feihu Huang, et al. ∙

research

∙ 06/30/2021

AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization

In the paper, we propose a class of faster adaptive gradient descent asc...

0 Feihu Huang, et al. ∙

research

∙ 06/23/2021

Bregman Gradient Policy Optimization

In this paper, we design a novel Bregman gradient policy optimization fr...

0 Feihu Huang, et al. ∙

research

∙ 06/21/2021

BiAdam: Fast Adaptive Bilevel Optimization Methods

Bilevel optimization recently has attracted increased interest in machin...

0 Feihu Huang, et al. ∙

research

∙ 06/21/2021

Compositional Federated Learning: Applications in Distributionally Robust Averaging and Meta Learning

In the paper, we propose an effective and efficient Compositional Federa...

7 Feihu Huang, et al. ∙

research

∙ 06/15/2021

SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients

Adaptive gradient methods have shown excellent performance for solving m...

13 Feihu Huang, et al. ∙

research

∙ 02/09/2021

A New Framework for Variance-Reduced Hamiltonian Monte Carlo

We propose a new framework of variance-reduced Hamiltonian Monte Carlo (...

0 Zhengmian Hu, et al. ∙

research

∙ 10/13/2020

Gradient Descent Ascent for Min-Max Problems on Riemannian Manifold

In the paper, we study a class of useful non-convex minimax optimization...

0 Feihu Huang, et al. ∙

research

∙ 08/18/2020

Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization

In the paper, we propose a new accelerated zeroth-order momentum (Acc-ZO...

0 Feihu Huang, et al. ∙

research

∙ 08/04/2020

Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization

In this paper, we propose a faster stochastic alternating direction meth...

0 Feihu Huang, et al. ∙

research

∙ 07/13/2020

Momentum-Based Policy Gradient Methods

In the paper, we propose a class of efficient momentum-based policy grad...

0 Feihu Huang, et al. ∙

research

∙ 07/30/2019

Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity

Zeroth-order (gradient-free) method is a class of powerful optimization ...

0 Feihu Huang, et al. ∙

research

∙ 05/29/2019

Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization

Alternating direction method of multipliers (ADMM) is a popular optimiza...

0 Feihu Huang, et al. ∙

research

∙ 02/16/2019

Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization

Proximal gradient method has been playing an important role to solve man...

0 Feihu Huang, et al. ∙

research

∙ 02/08/2018

Mini-Batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization

In the paper, we study the mini-batch stochastic ADMMs (alternating dire...

0 Feihu Huang, et al. ∙

research

∙ 04/26/2017

Linear Convergence of Accelerated Stochastic Gradient Descent for Nonconvex Nonsmooth Optimization

In this paper, we study the stochastic gradient descent (SGD) method for...

0 Feihu Huang, et al. ∙

research

∙ 10/10/2016

Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization

In the paper, we study the stochastic alternating direction method of mu...

0 Feihu Huang, et al. ∙

Feihu Huang

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