02 Dec MLG researchers obtain Best Paper Award at ICCCI 2020
Axel Abels and Tom Lenaerts were, together with their collaborators, awarded with the Best Paper award at the International Computational Collective Intelligence (ICCCI) for the paper entitled “How expert confidence can improve collective decision-making in contextual multi-armed bandit problems. Below the abstract of the paper. This work was supported by a FRIA grant awarded by Fondation de la Recherche Scientifique (F.R.S.-FNRS) of the Walloon-Brussels Federation.
In collective decision-making (CDM) a group of experts with a shared set of values and a common goal must combine their knowledge to make a collectively optimal decision. Whereas existing research on CDM primarily focuses on making binary decisions, we focus here on CDM applied to solving contextual multi-armed bandit (CMAB) prob- lems, where the goal is to exploit contextual information to select the best arm among a set. To address the limiting assumptions of prior work, we introduce confidence estimates and propose a novel approach to de- ciding with expert advice which can take advantage of these estimates. We further show that, when confidence estimates are imperfect, the pro- posed approach is more robust than the classical confidence-weighted majority vote.