Master’s Thesis Presentation • Machine Learning • On Computable Online LearningExport this event to calendar

Friday, April 21, 2023 — 1:30 PM to 2:30 PM EDT

Please note: This master’s thesis presentation will take place online.

Niki Hasrati, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Shai Ben-David

We initiate a study of computable online (c-online) learning, which we analyze under varying requirements for “optimality” in terms of the mistake bound. Our main contribution is to give a necessary and sufficient condition for optimal c-online learning and show that the Littlestone dimension no longer characterizes the optimal mistake bound of c-online learning. Furthermore, we introduce anytime optimal (a-optimal) online learning, a more natural conceptualization of “optimality” and a generalization of Littlestone’s Standard Optimal Algorithm. We show the existence of a computational separation between a-optimal and optimal online learning, proving that a-optimal online learning is computationally more difficult. Finally, we consider online learning with no requirements for optimality, and show, under a weaker notion of computability, that the finiteness of the Littlestone dimension no longer characterizes whether a class is c-online learnable with finite mistake bound. A potential avenue for strengthening this result is suggested by exploring the relationship between c-online and CPAC learning, where we show that c-online learning is as difficult as improper CPAC learning.


To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/95776648240.

Location 
Online master’s thesis presentation
200 University Avenue West

Waterloo, ON N2L 3G1
Canada
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