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Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
, CWI Amsterdam
Date: Thursday, June 30, 2016
Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone
Location: Seminar Room D507
Host: Stefanie Jegelka
Contact: Stefanie S. Jegelka, firstname.lastname@example.org
Relevant URL: http://wouterkoolen.info/
Speaker URL: None
email@example.com, firstname.lastname@example.org, email@example.com
TALK: Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
We consider online learning algorithms that guarantee worst-case regret rates in adversarial environments (so they can be deployed safely and will perform robustly), yet adapt optimally to favorable stochastic environments (so they will perform well in a variety of settings of practical importance). We quantify the friendliness of stochastic environments by means of the well-known Bernstein (a.k.a. generalized Tsybakov margin) condition. For two recent algorithms (Squint for the Hedge setting and MetaGrad for online convex optimization) we show that the particular form of their data-dependent individual-sequence regret guarantees implies that they adapt automatically to the Bernstein parameters of the stochastic environment. We prove that these algorithms attain fast rates in their respective settings both in expectation and with high probability.
Created by Stefanie S. Jegelka at Thursday, June 23, 2016 at 7:18 AM.