SCS Recruiting Seminar: Vatsal Sharan

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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.



  • Workflow Status:Published
  • Created By:Tess Malone
  • Created:04/02/2020
  • Modified By:Tess Malone
  • Modified:04/02/2020


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