Agnostic Insurability of Model Classes
Abstract
Motivated by problems in insurance, our task is to predict finite upper bounds on a future draw from an unknown distribution $p$ over natural numbers. We can only use past observations generated independently and identically distributed according to $p$. While $p$ is unknown, it is known to belong to a given collection $\mathcal{P}$ of probability distributions on the natural numbers.
Cite
Text
Santhanam and Anantharam. "Agnostic Insurability of Model Classes." Journal of Machine Learning Research, 2015.Markdown
[Santhanam and Anantharam. "Agnostic Insurability of Model Classes." Journal of Machine Learning Research, 2015.](https://mlanthology.org/jmlr/2015/santhanam2015jmlr-agnostic/)BibTeX
@article{santhanam2015jmlr-agnostic,
title = {{Agnostic Insurability of Model Classes}},
author = {Santhanam, Narayana and Anantharam, Venkat},
journal = {Journal of Machine Learning Research},
year = {2015},
pages = {2329-2355},
volume = {16},
url = {https://mlanthology.org/jmlr/2015/santhanam2015jmlr-agnostic/}
}