2024-09-12-drago24a.md

title	abstract	openreview	software	section	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
Bandits with Knapsacks and Predictions	We study the Bandits with Knapsacks problem with the aim of designing a learning-augmented online learning algorithm upholding better regret guarantees than the state-of-the-art primal-dual algorithms with worst-case guarantees, under both stochastic and adversarial inputs. In the adversarial case, we obtain better competitive ratios when the input predictions are accurate, while also maintaining worst-case guarantees for imprecise predictions. We introduce two algorithms tailored for the full and bandit feedback settings, respectively. Both algorithms integrate a static prediction with a worst-case no-$\alpha$-regret algorithm. This yields an optimized competitive ratio of $(\pi + (1 -\pi)/\alpha)^{-1}$ in scenarios where the prediction is perfect, and a competitive ratio of $\alpha/(1 - \pi)$ in the case of highly imprecise predictions, where $\pi \in (0,1)$ is chosen by the learner and $\alpha$ is Slater’s parameter. We complement this analysis by studying the stochastic setting under full feedback. We provide an algorithm which guarantees a pseudo-regret of $\widetilde{O}(\sqrt{T})$ with poor predictions, and 0 pseudo-regret with perfect predictions. We also characterize the smoothness of the algorithm.	pGnoioQ9z1	https://github.com/davidedrago0007/AdversarialBanditwithKnapsacksandPredictions	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	drago24a	0	Bandits with Knapsacks and Predictions	1189	1206	1189-1206	1189	false	Drago, Davide and Celli, Andrea and Elias, Marek	given family Davide Drago given family Andrea Celli given family Marek Elias	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/drago24a/drago24a.pdf