Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

Methods for bandit learning from user interactions often require a model of the reward a
certain context-action pair will yield–for example, the probability of a click on a …

Personalized real-time recommendation has had a profound impact on retail, media,
entertainment and other industries. However, developing recommender systems for every …

This paper introduces a new principled approach for off-policy learning in contextual
bandits. Unlike previous work, our approach does not derive learning principles from …

Modern recommender systems are often modelled under the sequential decision-making
paradigm, where the system decides which recommendations to show in order to maximise …

Conventional approaches to recommendation often do not explicitly take into account
information on previously shown recommendations and their recorded responses. One …

In recent years, a new line of research has taken an interventional view of recommender
systems, where recommendations are viewed as actions that the system takes to have a …

We study off-policy learning (OPL) of contextual bandit policies in large discrete action
spaces where existing methods--most of which rely crucially on reward-regression models …

Ad-load balancing is a critical challenge in online advertising systems, particularly in the
context of social media platforms, where the goal is to maximize user engagement and …

We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning
from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization …