SCS Recruiting Seminar: Vatsal Sharan

Thursday, April 9, 2020 - 11:00am to 12:00pm

Event Details

TITLE: Modern Perspectives on Classical Learning Problems: Role of Memory and Data Amplification


This talk will discuss statistical and computation requirements — and how they interact — for three learning setups. In the first part, we inspect the role of memory in learning. We study how the total memory available to a learning algorithm affects the amount of data needed for learning (or optimization), beginning by considering the fundamental problem of linear regression. Next, we examine the role of long-term memory vs. short-term memory for the task of predicting the next observation in a sequence given the past observations. Finally, we explore the statistical requirements for the task of manufacturing more data — namely how to generate a larger set of samples from an unknown distribution. Can “amplifying” a dataset be easier than learning?


Vatsal Sharan is a Ph.D. student at Stanford, advised by Greg Valiant. He is a part of the Theory group and the Statistical Machine Learning group, and his primary interests are in the theory and practice of machine learning.


For More Information Contact

Tess Malone, Communications Officer