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Adversarial bandit problems: the power of randomization

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Barcelona 2010. In this tutorial we discuss sequential prediction problems in which the forecaster has limited information about the past outcomes of the sequence. We concentrate on the so-called "adversarial" framework in which no probabilistic is available for the sequence. We describe various models of limited feedback and pay special attention to the so-called "multi-armed bandit" problem. We discuss various randomized prediction methods and analyze their behavior.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 8, 2015
Date Modified in MERLOT:: February 8, 2015
Author:: Gabor Lugosi, Department of Economics and Business, Pompeu Fabra University
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States