9 citations · 26 across the 13 of their papers we have counts for
14 papers
Smoothed Gradient Method for Nonconvex Federated Stochastic Bilevel Optimization
Xinwen Zhang, Peiran Yu, Zhaosong Lu +1
In recent years, federated stochastic bilevel optimization has attracted increasing attention due to its wide range of applications in machine learning. To reduce the computational…
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
Yingying Zhang, Kun Zhao, Guodong Liu +10
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosi…
Gradient-Free Method for Heavily Constrained Nonconvex Optimization
Wanli Shi, Hongchang Gao, Bin Gu
Zeroth-order (ZO) method has been shown to be a powerful method for solving the optimization problem where explicit expression of the gradients is difficult or infeasible to obtain…
On the Communication Complexity of Decentralized Stochastic Bilevel Optimization
Yihan Zhang, My T. Thai, Jie Wu +1
Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural architecture search. To extend s…
Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization
Hongchang Gao
The stochastic compositional minimax problem has attracted a surge of attention in recent years since it covers many emerging machine learning models. Meanwhile, due to the emergen…
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Dong Lu, Zhiqiang Wang, Teng Wang +3
Vision-language pre-training (VLP) models have shown vulnerability to adversarial examples in multimodal tasks. Furthermore, malicious adversaries can be deliberately transferred t…
When Decentralized Optimization Meets Federated Learning
Hongchang Gao, My T. Thai, Jie Wu
Federated learning is a new learning paradigm for extracting knowledge from distributed data. Due to its favorable properties in preserving privacy and saving communication costs,…
Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems?
Yihan Zhang, Wenhao Jiang, Feng Zheng +3
Decentralized minimax optimization has been actively studied in the past few years due to its application in a wide range of machine learning models. However, the current theoretic…
Federated Compositional Deep AUC Maximization
Xinwen Zhang, Yihan Zhang, Tianbao Yang +2
Federated learning has attracted increasing attention due to the promise of balancing privacy and large-scale learning; numerous approaches have been proposed. However, most existi…
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems
Hongchang Gao
Minimax optimization problems have attracted significant attention in recent years due to their widespread application in numerous machine learning models. To solve the minimax pro…
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network
Hongchang Gao, Bin Gu, My T. Thai
Bilevel optimization has been applied to a wide variety of machine learning models, and numerous stochastic bilevel optimization algorithms have been developed in recent years. How…
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems
Hongchang Gao
In this paper, we studied the federated bilevel optimization problem, which has widespread applications in machine learning. In particular, we developed two momentum-based algorith…
Periodic Stochastic Gradient Descent with Momentum for Decentralized Training
Hongchang Gao, Heng Huang
Decentralized training has been actively studied in recent years. Although a wide variety of methods have been proposed, yet the decentralized momentum SGD method is still underexp…
Adaptive Serverless Learning
Hongchang Gao, Heng Huang
With the emergence of distributed data, training machine learning models in the serverless manner has attracted increasing attention in recent years. Numerous training approaches h…