60 citations · 541 across the 61 of their papers we have counts for
15 papers · 1 filter
A Fully Single Loop Algorithm for Bilevel Optimization without Hessian Inverse
Junyi Li, Bin Gu, Heng Huang
In this paper, we propose a new Hessian inverse free Fully Single Loop Algorithm (FSLA) for bilevel optimization problems. Classic algorithms for bilevel optimization admit a doubl…
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels
Jiexi Yan, Lei Luo, Cheng Deng +1
Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to nois…
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm for First-order and Zeroth-order Optimization
Xiyuan Wei, Bin Gu, Heng Huang
The conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community due to its projection-free property…
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…
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…
Learning Sampling Policy for Faster Derivative Free Optimization
Zhou Zhai, Bin Gu, Heng Huang
Zeroth-order (ZO, also known as derivative-free) methods, which estimate the gradient only by two function evaluations, have attracted much attention recently because of its broad…
Doubly Contrastive Deep Clustering
Zhiyuan Dang, Cheng Deng, Xu Yang +1
Deep clustering successfully provides more effective features than conventional ones and thus becomes an important technique in current unsupervised learning. However, most deep cl…
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Qingsong Zhang, Bin Gu, Cheng Deng +1
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL a…
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Bin Gu, Guodong Liu, Yanfu Zhang +2
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability. Black-box o…
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…
A Bayesian Federated Learning Framework with Online Laplace Approximation
Liangxi Liu, Xi Jiang, Feng Zheng +4
Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to s…