16 citations · 38 across the 11 of their papers we have counts for
11 papers
Communication-Efficient Federated Group Distributionally Robust Optimization
Zhishuai Guo, Tianbao Yang
Federated learning faces challenges due to the heterogeneity in data volumes and distributions at different clients, which can compromise model generalization ability to various di…
Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization
Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo +2
In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve lower level problems and have important applications in machine learn…
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Zhishuai Guo, Rong Jin, Jiebo Luo +1
In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective…
A Novel Convergence Analysis for Algorithms of the Adam Family
Zhishuai Guo, Yi Xu, Wotao Yin +2
Since its invention in 2014, the Adam optimizer has received tremendous attention. On one hand, it has been widely used in deep learning and many variants have been proposed, while…
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization
Zhishuai Guo, Quanqi Hu, Lijun Zhang +1
In this paper, we consider non-convex stochastic bilevel optimization (SBO) problems that have many applications in machine learning. Although numerous studies have proposed stocha…
Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimator
Zhishuai Guo, Yi Xu, Wotao Yin +2
Although adaptive optimization algorithms have been successful in many applications, there are still some mysteries in terms of convergence analysis that have not been unraveled. T…
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
Zhuoning Yuan, Zhishuai Guo, Yi Xu +2
Deep AUC (area under the ROC curve) Maximization (DAM) has attracted much attention recently due to its great potential for imbalanced data classification. However, the research on…
An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives
Qi Qi, Zhishuai Guo, Yi Xu +2
In this paper, we propose a practical online method for solving a class of distributionally robust optimization (DRO) with non-convex objectives, which has important applications i…
Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition
Zhishuai Guo, Yan Yan, Zhuoning Yuan +1
This paper focuses on stochastic methods for solving smooth non-convex strongly-concave min-max problems, which have received increasing attention due to their potential applicatio…
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan +3
In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been…
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives
Zhishuai Guo, Yan Yan, Tianbao Yang
Stochastic gradient descent (SGD) has been widely studied in the literature from different angles, and is commonly employed for solving many big data machine learning problems. How…