Games for Computation and Learning

Abstract

This proposal is to further develop a game theoretic approach to numerical approximation and algorithm design and apply it to the design of machine learning algorithms.Prior work on this game theoretic approach has lead to the discoveries of (i) wavelets adaptedto arbitrary linear operators (gamblets) (ii) scalable solvers with some degree of universality (iii) new tools for numerical analysis and algorithm design such as the Fast Gamblet Transform (FGT).

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 28, 2018
Source ID
FA95501810271

Entities

People

  • Houman Owhadi

Organizations

  • Air Force Office of Scientific Research
  • California Institute of Technology
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Distributed Systems and Data Platform Development
  • Game Theory.

Technology Areas

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