2024-04-18-achddou24a.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
Multitask Online Learning: Listen to the Neighborhood Buzz	We study multitask online learning in a setting where agents can only exchange information with their neighbors on an arbitrary communication network. We introduce MT-CO\textsubscript{2}OL, a decentralized algorithm for this setting whose regret depends on the interplay between the task similarities and the network structure. Our analysis shows that the regret of MT-CO\textsubscript{2}OL is never worse (up to constants) than the bound obtained when agents do not share information. On the other hand, our bounds significantly improve when neighboring agents operate on similar tasks. In addition, we prove that our algorithm can be made differentially private with a negligible impact on the regret. Finally, we provide experimental support for our theory.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	achddou24a	0	Multitask Online Learning: Listen to the Neighborhood Buzz	1846	1854	1846-1854	1846	false	Achddou, Juliette and Cesa-Bianchi, Nicol\`{o} and Laforgue, Pierre	given family Juliette Achddou given family Nicolò Cesa-Bianchi given family Pierre Laforgue	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/achddou24a/achddou24a.pdf