Robustness meets algorithms

Abstract

In every corner of machine learning and statistics, there is a need for estimators that work not just in an idealized model, but even when their assumptions are violated. Unfortunately, in high dimensions, being provably robust and being efficiently computable are often at odds with each other.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 26, 2021
Source ID
10.1145/3453935

Entities

People

  • Alistair Stewart
  • Ankur Moitra
  • Daniel M. Kane
  • Gautam Kamath
  • Ilias Diakonikolas
  • Jerry Li

Organizations

  • Alfred P. Sloan Foundation
  • David and Lucile Packard Foundation
  • Google
  • Massachusetts Institute of Technology
  • Microsoft
  • National Science Foundation
  • Office of Naval Research
  • University of California, San Diego
  • University of Waterloo
  • University of Wisconsin–Madison

Tags

Fields of Study

  • Computer science

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms