290 citations · 492 across the 36 of their papers we have counts for
4 papers · 1 filter
Shifted Composition III: Local Error Framework for KL Divergence
Jason M. Altschuler, Sinho Chewi
Coupling arguments are a central tool for bounding the deviation between two stochastic processes, but traditionally have been limited to Wasserstein metrics. In this paper, we app…
Acceleration by Random Stepsizes: Hedging, Equalization, and the Arcsine Stepsize Schedule
Jason M. Altschuler, Pablo A. Parrilo
We show that for separable convex optimization, random stepsizes fully accelerate Gradient Descent. Specifically, using inverse stepsizes i.i.d. from the Arcsine distribution impro…
Accelerating Proximal Gradient Descent via Silver Stepsizes
Jinho Bok, Jason M. Altschuler
Surprisingly, recent work has shown that gradient descent can be accelerated without using momentum -- just by judiciously choosing stepsizes. An open question raised by several pa…
Shifted Interpolation for Differential Privacy
Jinho Bok, Weijie Su, Jason M. Altschuler
Noisy gradient descent and its variants are the predominant algorithms for differentially private machine learning. It is a fundamental question to quantify their privacy leakage,…