PhD Seminar • Data Systems • Honest Hyperparameter Tuning for Private LearningExport this event to calendar

Wednesday, November 2, 2022 — 12:00 PM to 1:00 PM EDT

Please note: This PhD seminar will take place online.

Shubhankar Mohapatra, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Xi He

Hyperparameter optimization is a ubiquitous challenge in machine learning, and the performance of a trained model depends crucially upon their effective selection. While a rich set of tools exist for this purpose, there are currently no practical hyperparameter selection methods under the constraint of differential privacy (DP). We study honest hyperparameter selection for differentially private machine learning, in which the process of hyperparameter tuning is accounted for in the overall privacy budget.

To this end, we i) show that standard composition tools outperform more advanced techniques in many settings, ii) empirically and theoretically demonstrate an intrinsic connection between the learning rate and clipping norm hyperparameters, iii) show that adaptive optimizers like DPAdam enjoy a significant advantage in the process of honest hyperparameter tuning, and iv) draw upon novel limiting behaviour of Adam in the DP setting to design a new and more efficient optimizer.


To join this PhD seminar on Zoom, please go to https://uwaterloo.zoom.us/j/94349250988.

Location 
Online PhD seminar
200 University Avenue West

Waterloo, ON N2L 3G1
Canada
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