TY  - JOUR
AV  - public
VL  - 23
Y1  - 2021/11/18/
TI  - Still no free lunches: the price to pay for tighter PAC-Bayes bounds
IS  - 11
N1  - 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.
UR  - https://doi.org/10.3390/e23111529
N2  - ?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.
ID  - discovery10083912
A1  - Guedj, B
A1  - Pujol, L
KW  - statistical learning theory; PAC-Bayes theory; no free lunch theorems
JF  - Entropy
ER  -