When:
Wednesday, February 26, 2020
12:00 PM - 1:00 PM CT
Where: Mudd Hall ( formerly Seeley G. Mudd Library), Room 3514, 2233 Tech Drive, Evanston, IL 60208 map it
Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students
Contact:
Pam Villalovoz
(847) 467-6558
Group: Department of Computer Science (CS)
Category: Academic, Lectures & Meetings
Title: Modern Perspectives on Classical Learning Problems: Role of Memory and Data Amplification
Abstract
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?
Biography
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.