1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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 …
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…
Differentially Private Online-to-Batch for Smooth Losses
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
We develop a new reduction that converts any online convex optimization algorithm suffering regret into an -differentially private stochastic convex optimization a…