Over the past two decades, machine learning has rapidly evolved and emerged as a highly influential discipline of
computer science and engineering. One of the pillars of machine learning is mathematical optimization, and the
connection between the two fields has been a primary focus of research. In this talk, I will present two recent works that
contribute to this study, focusing on online learning---a central model in machine learning for sequential decision
making and learning under uncertainty. In the first part of the talk, I will describe a foundational result concerned with
the power of optimization in online learning, and give answer to the question: does there exist a generic and efficient
reduction from online learning to black-box optimization? In the second part, I will discuss a recent work that employs
online learning techniques to design a new efficient and adaptive preconditioned algorithm for large-scale optimization.
Despite employing preconditioning, the algorithm is practical even in modern optimization scenarios such as those
arising in training state-of-the-art deep neural networks. I will present the new algorithm along with its theoretical
guarantees and demonstrate its performance empirically.
Short Bio:
Tomer Koren is a Research Scientist at Google, Mountain View. His research focuses on machine learning and
optimization, with an emphasis on online and statistical learning, sequential decision making, and stochastic
optimization. Tomer joined Google in 2016 after receiving his Ph.D. from the Technion---Israel Institute of
Technology, under the guidance of Prof. Elad Hazan. During his doctoral studies, he was also a research intern with
Microsoft Research Herzliya, Microsoft Research Redmond, and Yahoo Research Labs in Haifa.
The lecture will take place on Sunday, 17/12/2017
at 11:30 in room 1061
Electrical Eng. Building
Technion City
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