STATISTICAL LEARNING FOR COMPLEX DYNAMIC DATA SETS IN METRIC SPACES

Abstract

This research will consider a variety of situations where objects being estimated (functions or probability distributions or graphs or interaction laws in an interacting agent-based system) have special structure that may enable us to avoid the "curse of dimensionality". This well-known curse of dimensionality makes many estimation problems in high-dimensional spaces exponentially hard, statistically or computationally.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 12, 2021
Source ID
FA95502010288

Entities

People

  • Mauro Maggioni

Organizations

  • Air Force Office of Scientific Research
  • Johns Hopkins University
  • United States Air Force

Tags

Readers

  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • Space