eprintid: 10083912
rev_number: 35
eprint_status: archive
userid: 608
dir: disk0/10/08/39/12
datestamp: 2019-10-30 15:22:46
lastmod: 2022-04-20 12:08:35
status_changed: 2019-10-30 15:22:46
type: article
metadata_visibility: show
creators_name: Guedj, B
creators_name: Pujol, L
title: Still no free lunches: the price to pay for tighter PAC-Bayes bounds
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: statistical learning theory; PAC-Bayes theory; no free lunch theorems
note: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstract: “No free lunch” results state the impossibility of obtaining meaningful bounds on the error of a learning algorithm without prior assumptions and modelling, which is more or less realistic for a given problem. Some models are “expensive” (strong assumptions, such as sub-Gaussian tails), others are “cheap” (simply finite variance). As it is well known, the more you pay, the more you get: in other words, the most expensive models yield the more interesting bounds. Recent advances in robust statistics have investigated procedures to obtain tight bounds while keeping the cost of assumptions minimal. The present paper explores and exhibits what the limits are for obtaining tight probably approximately correct (PAC)-Bayes bounds in a robust setting for cheap models.
date: 2021-11-18
official_url: https://doi.org/10.3390/e23111529
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1708791
doi: 10.3390/e23111529
lyricists_name: Guedj, Benjamin
lyricists_id: BGUED94
actors_name: Guedj, Benjamin
actors_id: BGUED94
actors_role: owner
full_text_status: public
publication: Entropy
volume: 23
number: 11
article_number: 1529
citation:        Guedj, B;    Pujol, L;      (2021)    Still no free lunches: the price to pay for tighter PAC-Bayes bounds.                   Entropy , 23  (11)    , Article 1529.  10.3390/e23111529 <https://doi.org/10.3390/e23111529>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10083912/1/entropy-23-01529-v2.pdf