<ul data-eligibleForWebStory="false"><li>Recent research explores the combination of non-uniform exploration and supervised learning in decision-making systems to improve immediate performance while maintaining off-policy learning capabilities.</li><li>An analysis conducted at Adyen, a global payments processor, demonstrates that regression oracles can enhance system performance but may introduce challenges due to rigid algorithmic assumptions.</li><li>The study reveals that improvements in policy may lead to subsequent performance degradation due to shifts in reward distribution and increased class imbalance in training data.</li><li>There is a potential 'oscillation effect' identified where regression oracles influence probability estimates, impacting the stability and performance consistency of policy models over successive iterations.</li></ul>

Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning

Discover more