2008-07-09-hines08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Learning when to take advice: a statistical test for achieving a correlated equilibrium	We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We present an algorithm that each agent can use so that, with high probability, they can verify whether or not the mediator’s advice is useful. In particular, if the mediator’s advice is useful then agents will reach a correlated equilibrium, but if the mediator’s advice is not useful, then agents are not harmed by using our test, and can fall back to their original learning algorithm. We then generalize our algorithm and show that in the limit it always correctly verifies the mediator’s advice.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hines08a	0	Learning when to take advice: a statistical test for achieving a correlated equilibrium	274	281	274-281	274	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Hines, Greg and Larson, Kate	given family Greg Hines given family Kate Larson	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/hines08a/hines08a.pdf