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.