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Author Information
Dylan J Foster (Cornell University)
Satyen Kale (Google)
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.
Karthik Sridharan (Cornell University)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Poster: Parameter-Free Online Learning via Model Selection »
Wed. Dec 6th 02:30 -- 06:30 AM Room Pacific Ballroom #63
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2022 : Invited Talk #1, Differentially Private Learning with Margin Guarantees, Mehryar Mohri »
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2018 Poster: Uniform Convergence of Gradients for Non-Convex Learning and Optimization »
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2017 : Mehryar Mohri (NYU) on Tight Learning Bounds for Multi-Class Classification »
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2017 : (Invited Talk) Mehryar Mohri: Regret minimization against strategic buyers. »
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2016 Poster: Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters »
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2016 Poster: Learning in Games: Robustness of Fast Convergence »
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2016 Tutorial: Theory and Algorithms for Forecasting Non-Stationary Time Series »
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2015 Poster: Revenue Optimization against Strategic Buyers »
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2015 Poster: Learning Theory and Algorithms for Forecasting Non-stationary Time Series »
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