Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

We consider data in the form of pairwise comparisons of n items, with the goal of identifying
the top k items for some value of k< n, or alternatively, recovering a ranking of all the items …

In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …

We study the $ K $-armed contextual dueling bandit problem, a sequential decision making
setting in which the learner uses contextual information to make two decisions, but only …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

Bayesian optimization (BO) has emerged during the last few years as an effective approach
to optimize black-box functions where direct queries of the objective are expensive. We …

Active ranking from pairwise comparisons and when parametric assumptions do not help Page
1 The Annals of Statistics 2019, Vol. 47, No. 6, 3099–3126 https://doi.org/10.1214/18-AOS1772 …

In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit
problems. As its name suggests, D-TS selects both the first and the second candidates …

The dueling bandits problem is an online learning framework for learning from pairwise
preference feedback, and is particularly well-suited for modeling settings that elicit …

The dueling bandits problem is an online learning framework where learning happens “on-
thefly” through preference feedback, ie, from comparisons between a pair of actions. Unlike …