Entry Date:
January 29, 2018

Online Learning

Principal Investigator Alexander Rakhlin


We aim to develop robust prediction methods that do not rely on the i.i.d. or stationary nature of data. In contrast to the well-studied setting of Statistical Learning, methods that predict in an online fashion are arguably more complex and nontrivial. Major questions that arise in this setting are: (a) How to model the problem at hand? (b) How many examples are required to achieve certain level of performance, and what are the computationally-efficient methods? (c) How to deal with incomplete feedback and the exploration-exploitation dilemma? Examples: sequentially predicting users' preferences, classifying nodes in a social network, sequentially selecting medical treatment strategies while observing limited feedback about the past decisions, etc.