Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

We consider the adversarial multi-armed bandit problem under delayed feedback. We
analyze variants of the Exp3 algorithm that tune their step size using only information (about …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Online recommender systems often face long delays in receiving feedback, especially when
optimizing for some long-term metrics. While mitigating the effects of delays in learning is …

Reinforcement Learning (RL) is a branch of machine learning (ML) that is used to train
artificial intelligence (AI) systems and find the optimal solution for problems. This tutorial …

In supervised machine learning, privileged information (PI) is information that is unavailable
at inference, but is accessible during training time. Research on learning using privileged …

We investigate a nonstochastic bandit setting in which the loss of an action is not
immediately charged to the player, but rather spread over the subsequent rounds in an …

We study a $ K $-armed bandit with delayed feedback and intermediate observations. We
consider a model where intermediate observations have a form of a finite state, which is …

Predicting the expected value or number of post-click conversions (purchases or other
events) is a key task in performance-based digital advertising. In training a conversion …

This doctoral thesis covers various aspects of theoretical machine learning relative to two of
its most fundamental paradigms: batch learning and online learning. In particular, we …