In late July and early August, the Allen School was pleased to host a very successful MSRI Mathematics of Machine Learning Summer School. Around 40 Ph.D. students from across the country — and beyond — were brought to the University of Washington for two weeks of lectures by experts in the field, as well as problem sessions and social events. The lectures were also opened up to the broader UW community and recorded for future use.
Learning theory is a rich field at the intersection of statistics, probability, computer science, and optimization. Over the last few decades the statistical learning approach has been successfully applied to many problems of great interest, such as bioinformatics, computer vision, speech processing, robotics, and information retrieval. These impressive successes relied crucially on the mathematical foundation of statistical learning.
Recently, deep neural networks have demonstrated stunning empirical results across many applications like vision, natural language processing, and reinforcement learning. The field is now booming with new mathematical problems, and in particular, the challenge of providing theoretical foundations for deep learning techniques is still largely open. On the other hand, learning theory already has a rich history, with many beautiful connections to various areas of mathematics such as probability theory, high dimensional geometry, and game theory. The purpose of the summer school was to introduce graduate students and advanced undergraduates to these foundational results, as well as to expose them to the new and exciting modern challenges that arise in deep learning and reinforcement learning.
Participants explored a variety of topics with the guidance of lecturers Joan Bruna, a professor at New York University (deep learning); Stanford University professor Emma Brunskill (reinforcement learning); Sébastien Bubeck, senior researcher at Microsoft Research (convex optimization); Allen School professor Kevin Jamieson (bandits); and Robert Schapire, principal researcher at Microsoft Research (statistical learning theory).
The summer school was made possible by support from the Mathematical Sciences Research Institute, Microsoft Research, and the Allen School, with the cooperation of the Algorithmic Foundations of Data Science Institute at the University of Washington. The program was organized by Bubeck and Adith Swaminathan of Microsoft Research and Allen School professor Anna Karlin.