Computer Sciences Colloquium - Interplays between Machine Learning and Optimization
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.
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.