The contextual bandit has been identified as a powerful framework to formulate the
recommendation process as a sequential decision-making process, where each item is …

Recommender systems are an important part of the modern human experience whose
influence ranges from the food we eat to the news we read. Yet, there is still debate as to …

Short-and long-term outcomes of an algorithm often differ, with damaging downstream
effects. A known example is a click-bait algorithm, which may increase short-term clicks but …

Contextual bandits serve as a fundamental algorithmic framework for optimizing
recommendation decisions online. Though extensive attention has been paid to tailoring …

This paper discusses the system architecture design and deployment of non-stationary multi-
armed bandit approaches to determine a near-optimal payment routing policy based on the …

This manuscript explores the intersection of surrogate outcomes and adaptive designs in
statistical research. While surrogate outcomes have long been studied for their potential to …

Performing policy evaluation in education, healthcare and online commerce can be
challenging, because it can require waiting substantial amounts of time to observe outcomes …

In the dynamic landscape of online businesses, recommender systems are pivotal in
enhancing user experiences. While traditional approaches have relied on static supervised …

From incorporating LLMs in education, to identifying new drugs and improving ways to
charge batteries, innovators constantly try new strategies in search of better long-term …

Recommendation systems are widespread, and through customized recommendations,
promise to match users with options they will like. To that end, data on engagement is …