163 citations · 1.2k across the 128 of their papers we have counts for
28 papers · 1 filter
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…
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization
Quanqi Hu, Yongjian Zhong, Tianbao Yang
In this paper, we study multi-block min-max bilevel optimization problems, where the upper level is non-convex strongly-concave minimax objective and the lower level is a strongly…
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications
Bokun Wang, Tianbao Yang
This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index. W…
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…
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan, Yi Xu, Qihang Lin +2
Epoch gradient descent method (a.k.a. Epoch-GD) proposed by Hazan and Kale (2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal…