Notes on computational-to-statistical gaps: predictions using statistical physics
Machine Learning
2018-04-23 v2 Data Structures and Algorithms
Machine Learning
Abstract
In these notes we describe heuristics to predict computational-to-statistical gaps in certain statistical problems. These are regimes in which the underlying statistical problem is information-theoretically possible although no efficient algorithm exists, rendering the problem essentially unsolvable for large instances. The methods we describe here are based on mature, albeit non-rigorous, tools from statistical physics. These notes are based on a lecture series given by the authors at the Courant Institute of Mathematical Sciences in New York City, on May 16th, 2017.
Cite
@article{arxiv.1803.11132,
title = {Notes on computational-to-statistical gaps: predictions using statistical physics},
author = {Afonso S. Bandeira and Amelia Perry and Alexander S. Wein},
journal= {arXiv preprint arXiv:1803.11132},
year = {2018}
}
Comments
22 pages, 2 figures