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 …

The dramatic improvements in core information retrieval tasks engendered by neural
rankers create a need for novel evaluation methods. If every ranker returns highly relevant …

Relevance ranking and result diversification are two core areas in modern recommender
systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item …

A core step in production model research and development involves the offline evaluation of
a system before production deployment. Traditional offline evaluation of search …

We study the $ K $-armed dueling bandit problem, a variation of the traditional multi-armed
bandit problem in which feedback is obtained in the form of pairwise comparisons. Previous …

Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived
from users' interactions, such as clicks. Clicks however are a biased signal: specifically, top …

We study the non-stationary dueling bandits problem with $ K $ arms, where the time
horizon $ T $ consists of $ M $ stationary segments, each of which is associated with its own …

Several algorithms for finding the best arm in the dueling bandits setting assume the
existence of a Condorcet winner (CW), that is, an arm that uniformly dominates all other …

Optimization problems find widespread use in both single-objective and multi-objective
scenarios. In practical applications, users aspire for solutions that converge to the region of …

In this thesis, we study the role of adaptivity in decision-making problems under uncertainty.
The first part of the thesis focuses on combinatorial problems, while the second part of the …