23 citations · 119 across the 32 of their papers we have counts for
33 papers
Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Level
Yicong Jiang, Feihu Huang
Multi-objective bilevel optimization has wide applications in the AI area such as automated learning and multi-task meta-learning. Although recently some works have been begun to s…
Federated Compositional Muon Optimizer for Matrix-Wise Models
Wang Yan, Feihu Huang
Muon, a more recently developed optimizer, is useful for matrix-wise models in AI areas. Although many works have studied Muon and its variants, these methods are still not particu…
Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems
Feihu Huang, Songcan Chen
Minimax optimization plays an important role in many machine learning tasks such as generative adversarial networks (GANs) and adversarial training. Although recently a wide variet…
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
Feihu Huang, Chunyu Xuan, Xinrui Wang +2
Minimax optimization recently is widely applied in many machine learning tasks such as generative adversarial networks, robust learning and reinforcement learning. In the paper, we…
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level
Feihu Huang
Bilevel optimization is a popular two-level hierarchical optimization, which has been widely applied to many machine learning tasks such as hyperparameter learning, meta learning a…
FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging
Junyi Li, Feihu Huang, Heng Huang
Federated learning (FL) is an emerging learning paradigm to tackle massively distributed data. In Federated Learning, a set of clients jointly perform a machine learning task under…
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
Junyi Li, Feihu Huang, Heng Huang
Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms. However, its application in the Federated Learning setting remains relatively u…
Faster Adaptive Federated Learning
Xidong Wu, Feihu Huang, Zhengmian Hu +1
Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated learning algorithms have been proposed for the non-convex di…
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…
Adaptive Federated Minimax Optimization with Lower Complexities
Feihu Huang, Xinrui Wang, Junyi Li +1
Federated learning is a popular distributed and privacy-preserving learning paradigm in machine learning. Recently, some federated learning algorithms have been proposed to solve t…
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization
Feihu Huang
Federated Learning is a popular distributed learning paradigm in machine learning. Meanwhile, composition optimization is an effective hierarchical learning model, which appears in…
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…
Dateformer: Time-modeling Transformer for Longer-term Series Forecasting
Julong Young, Junhui Chen, Feihu Huang +1
Transformers have demonstrated impressive strength in long-term series forecasting. Existing prediction research mostly focused on mapping past short sub-series (lookback window) t…
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…
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…
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…
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…