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Poster
Online Learning with Transductive Regret
Scott Yang · Mehryar Mohri

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #65 #None

We study online learning with the general notion of transductive regret, that is regret with modification rules applying to expert sequences (as opposed to single experts) that are representable by weighted finite-state transducers. We show how transductive regret generalizes existing notions of regret, including: (1) external regret; (2) internal regret; (3) swap regret; and (4) conditional swap regret. We present a general and efficient online learning algorithm for minimizing transductive regret. We further extend that to design efficient algorithms for the time-selection and sleeping expert settings. A by-product of our study is an algorithm for swap regret, which, under mild assumptions, is more efficient than existing ones, and a substantially more efficient algorithm for time selection swap regret.

Author Information

Scott Yang (D. E. Shaw & Co.)
Mehryar Mohri (Courant Institute and Google)

Mehryar Mohri is a Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences and a Research Consultant at Google. Prior to these positions, he spent about ten years at AT&T Bell Labs, later AT&T Labs-Research, where he served for several years as a Department Head and a Technology Leader. His research interests cover a number of different areas: primarily machine learning, algorithms and theory, automata theory, speech processing, natural language processing, and also computational biology. His research in learning theory and algorithms has been used in a variety of applications. His work on automata theory and algorithms has served as the foundation for several applications in language processing, with several of his algorithms used in virtually all spoken-dialog and speech recognitions systems used in the United States. He has co-authored several software libraries widely used in research and academic labs. He is also co-author of the machine learning textbook Foundations of Machine Learning used in graduate courses on machine learning in several universities and corporate research laboratories.

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