When:
Friday, February 17, 2023
11:00 AM - 12:00 PM CT
Where: Chambers Hall, Ruan Conference Room – lower level , 600 Foster St, Evanston, IL 60208 map it
Audience: Faculty/Staff - Post Docs/Docs - Graduate Students
Contact:
Kisa Kowal
(847) 491-3974
Group: Department of Statistics and Data Science
Category: Academic, Lectures & Meetings
Learner-Private Convex Optimization
Dana Yang, Assistant Professor, Department of Statistics and Data Science, Cornell University
Abstract: Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function. The paradigm has gained significant popularity recently thanks to its scalability in large-scale optimization and machine learning. The repeated interactions, however, expose the learner to privacy risks from eavesdropping adversaries that observe the submitted queries. In this work, we study how to optimally obfuscate the learner’s queries in convex optimization with first-order feedback, so that their learned optimal value is provably difficult to estimate for the eavesdropping adversary.