3 citations · 11 across the 11 of their papers we have counts for
11 papers
Feed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization
Xuxing Chen, Yun He, Jiayi Xu +9
In machine learning, the goal of multi-task learning (MTL) is to optimize multiple objectives together. Recent works, for example, Multiple Gradient Descent Algorithm (MGDA) and it…
Fully First-Order Methods for Decentralized Bilevel Optimization
Xiaoyu Wang, Xuxing Chen, Shiqian Ma +1
This paper focuses on decentralized stochastic bilevel optimization (DSBO) where agents only communicate with their neighbors. We propose Decentralized Stochastic Gradient Descent…
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
Xuxing Chen, Abhishek Roy, Yifan Hu +1
We develop and analyze algorithms for instrumental variable regression by viewing the problem as a conditional stochastic optimization problem. In the context of least-squares inst…
Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization
Jiaxiang Li, Xuxing Chen, Shiqian Ma +1
Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually requ…
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression
Xuxing Chen, Krishnakumar Balasubramanian, Promit Ghosal +1
We conduct a comprehensive investigation into the dynamics of gradient descent using large-order constant step-sizes in the context of quadratic regression models. Within this fram…
Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning
Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi
We develop and analyze stochastic approximation algorithms for solving nested compositional bi-level optimization problems. These problems involve a nested composition of poten…
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions
Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian
Stochastic Bilevel optimization usually involves minimizing an upper-level (UL) function that is dependent on the arg-min of a strongly-convex lower-level (LL) function. Several al…
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization
Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian +1
We focus on decentralized stochastic non-convex optimization, where agents work together to optimize a composite objective function which is a sum of a smooth term and a non-sm…
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity
Xuxing Chen, Minhui Huang, Shiqian Ma +1
Bilevel optimization recently has received tremendous attention due to its great success in solving important machine learning problems like meta learning, reinforcement learning,…
Decentralized Bilevel Optimization
Xuxing Chen, Minhui Huang, Shiqian Ma
Bilevel optimization has been successfully applied to many important machine learning problems. Algorithms for solving bilevel optimization have been studied under various settings…
Efficiently Escaping Saddle Points in Bilevel Optimization
Minhui Huang, Xuxing Chen, Kaiyi Ji +2
Bilevel optimization is one of the fundamental problems in machine learning and optimization. Recent theoretical developments in bilevel optimization focus on finding the first-ord…