1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2026★ 1 cited
Random Scaling and Momentum for Non-smooth Non-convex Optimization
Qinzi Zhang, Ashok Cutkosky
Training neural networks requires optimizing a loss function that may be highly irregular, and in particular neither convex nor smooth. Popular training algorithms are based on sto…
cs.LG2025
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
Hoang Tran, Qinzi Zhang, Ashok Cutkosky
There is a significant gap between our theoretical understanding of optimization algorithms used in deep learning and their practical performance. Theoretical development usually f…
math.OC2024
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
We introduce a new zeroth-order algorithm for private stochastic optimization on nonconvex and nonsmooth objectives. Given a dataset of size , our algorithm ensures $(α,αÏ^2/…