Online Learning: Theory, Algorithms, and Applications

Shai Shalev‐Shwartz

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Advanced Bandit Algorithms Research Domain Adaptation and Few-Shot Learning Machine Learning and Algorithms

Abstract

“How much better to get wisdom than gold, to choose understanding rather than silver” [Proverbs 16:16] iv Acknowledgments First, I would like to express my deep gratitude to my advisor, Yoram Singer. Yoram has had a profound influence on my research. He exposed me to the fascinating world of online learning. I have learned a variety of research skills from Yoram including scientific writing, basic analysis tools, and (hopefully) good taste in choosing problems. Besides being a good teacher, Yoram is also a great friend. I would also like to thank the other members of my research committee: Jeff Rosenschein, Mike Werman, and Mira Balaban. I collaborated and wrote papers with great people. Thank you Yonatan Amit, Koby Crammer, Ofer Dekel,

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2006Prediction, Learning, and Games 2003Online convex programming and generalized infinitesimal gradient ascent 2000A Simple Adaptive Procedure Leading to Correlated Equilibrium 1997Exponentiated Gradient versus Gradient Descent for Linear Predictors 1956An analog of the minimax theorem for vector payoffs 1997How to use expert advice 1996Game theory, on-line prediction and boosting

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2013Regret in Online Combinatorial Optimization 2018Learning in games with continuous action sets and unknown payoff functions 2011On the Universality of Online Mirror Descent 2020Evolution and impact of bias in human and machine learning algorithm interaction 2019Online Learning: A Modern Introduction Using Convex Optimization 2011Introduction to Online Optimization

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