2024-04-18-deb24a.md

title	software	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
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources	https://github.com/rohandeb24/Dueling-Bandits-with-Constraints	We consider the problem of reward maximization in the dueling bandit setup along with constraints on resource consumption. As in the classic dueling bandits, at each round the learner has to choose a pair of items from a set of $K$ items and observe a relative feedback for the current pair. Additionally, for both items, the learner also observes a vector of resource consumptions. The objective of the learner is to maximize the cumulative reward, while ensuring that the total consumption of any resource is within the allocated budget. We show that due to the relative nature of the feedback, the problem is more difficult than its bandit counterpart and that without further assumptions the problem is not learnable from a regret minimization perspective. Thereafter, by exploiting assumptions on the available budget, we provide an EXP3 based dueling algorithm that also considers the associated consumptions and show that it achieves an $\tilde{\mathcal{O}}\left(\big({\frac{OPT^{(b)}}{B}}+1\big)K^{1/3}T^{2/3}\right)$ regret, where $OPT^{(b)}$ is the optimal value and $B$ is the available budget. Finally, we provide numerical simulations to demonstrate the efficacy of our proposed method.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	deb24a	0	Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources	4546	4554	4546-4554	4546	false	Deb, Rohan and Saha, Aadirupa and Banerjee, Arindam	given family Rohan Deb given family Aadirupa Saha given family Arindam Banerjee	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/deb24a/deb24a.pdf