2022-06-28-lattimore22a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini’s Regret	We show that a version of the generalised information ratio of Lattimore and Gyorgy (2020) determines the asymptotic minimax regret for all finite-action partial monitoring games provided that (a) the standard definition of regret is used but the latent space where the adversary plays is potentially infinite; or (b) the regret introduced by Rustichini (1999) is used and the latent space is finite. Our results are complemented by a number of examples. For any p ∈ [1/2, 1] there exists an infinite partial monitoring game for which the minimax regret over n rounds is n^p up to subpolynomial factors and there exist finite games for which the minimax Rustichini regret is n^(4/7) up to subpolynomial factors.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lattimore22a	0	Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini’s Regret	1547	1575	1547-1575	1547	false	Lattimore, Tor	given family Tor Lattimore	2022-06-28		Proceedings of Thirty Fifth Conference on Learning Theory	178	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v178/lattimore22a/lattimore22a.pdf