4 papers
Designing an Interpretable Interface for Contextual Bandits
Andrew Maher, Matia Gobbo, Lancelot Lachartre +3
Contextual bandits have become an increasingly popular solution for personalized recommender systems. Despite their growing use, the interpretability of these systems remains a sig…
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features
Rowan Swiers, Subash Prabanantham, Andrew Maher
Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the…
Rapid and Scalable Bayesian AB Testing
Srivas Chennu, Andrew Maher, Christian Pangerl +4
AB testing aids business operators with their decision making, and is considered the gold standard method for learning from data to improve digital user experiences. However, there…
Dynamic Memory for Interpretable Sequential Optimisation
Srivas Chennu, Andrew Maher, Jamie Martin +1
Real-world applications of reinforcement learning for recommendation and experimentation faces a practical challenge: the relative reward of different bandit arms can evolve over t…