2 citations · 6 across the 21 of their papers we have counts for
23 papers
Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization
Ruichen Jiang, Zakaria Mhammedi, Mehryar Mohri +1
We study online linear optimization with matrix variables constrained by the operator norm, a setting where the geometry renders designing data-dependent and efficient adaptive alg…
Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism
Francisco Patitucci, Ruichen Jiang, Aryan Mokhtari
A recent breakthrough in nonconvex optimization is the online-to-nonconvex conversion framework of [Cutkosky et al., 2023], which reformulates the task of finding an -…
On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani +1
In this paper, we study the problem of solving a simple bilevel optimization problem, where the upper-level objective is minimized over the solution set of the lower-level problem.…
Online Learning-guided Learning Rate Adaptation via Gradient Alignment
Ruichen Jiang, Ali Kavis, Aryan Mokhtari
The performance of an optimizer on large-scale deep learning models depends critically on fine-tuning the learning rate, often requiring an extensive grid search over base learning…
Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods
Ruichen Jiang, Aryan Mokhtari, Francisco Patitucci
We study the problem of finding an -first-order stationary point (FOSP) of a smooth function, given access only to gradient information. The best-known gradient query complexity…
Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence
Ruichen Jiang, Aryan Mokhtari
In this paper, we propose a quasi-Newton method for solving smooth and monotone nonlinear equations, including unconstrained minimization and minimax optimization as special cases.…