Ran Raz, Ph.D.Professor, Theoretical Computer Science, Princeton University
Weizmann Institute of Science
Mathematics and Physical Sciences lectures are open to the public and are held at the Gerald D. Fischbach Auditorium at the Simons Foundation headquarters in New York City. Tea is served prior to each lecture.
Can one prove unconditional lower bounds on the number of samples needed for learning, under memory constraints? A recent line of works shows that for a large class of learning problems, any learning algorithm requires either a memory of super-linear size or a super-polynomial number of samples. For example, any algorithm for learning parities of size n, from a stream of samples, requires either a memory of quadratic size or an exponential number of samples.
A main message of these works is that for some learning problems, access to a relatively large memory is crucial. Ran Raz will tell about some of these works and discuss relations to computational complexity and applications in bounded-storage cryptography.