Theory Lunch This Week (11/7, 12:00, SAL 322)

[Grayson York]

Hi all,

Please join us this week at theory lunch where Devansh Gupta will be speaking about the following work.

Thanks,

Grayson

Title: On the Inherent Privacy of Zeroth Order Projected SGD

Abstract: Differentially private zeroth-order optimization methods have recently gained popularity in private fine tuning of machine learning models due to their reduced memory requirements. Current approaches for privatizing zeroth-order methods rely on adding Gaussian noise to the estimated zeroth-order gradients. However, since the search direction in the zeroth-order methods is inherently random, researchers have raised an important question: is the inherent noise in zeroth-order estimators sufficient to ensure the overall differential privacy of the algorithm? This work settles this question  for a class of oracle-based optimization algorithms where the oracle returns  zeroth-order gradient estimates.

[Grayson York]

Hi all,

Please join us at the following zoom link:

https://usc.zoom.us/j/91081703080?pwd=v5aWtgSBcllDCT1Z5Q2rYnyfgF152S.1