3 papers
cs.LG2026
Identifiable Latent Bandits: Leveraging observational data for personalized decision-making
Ahmet Zahid BalcıoÄlu, Newton Mwai, Emil Carlsson +1
Sequential decision-making algorithms such as multi-armed bandits can find optimal personalized decisions, but are notoriously sample-hungry. In personalized medicine, for example,…
cs.LG2025
Latent Preference Bandits
Newton Mwai, Emil Carlsson, Fredrik D. Johansson
Bandit algorithms are guaranteed to solve diverse sequential decision-making problems, provided that a sufficient exploration budget is available. However, learning from scratch is…
cs.LG2025
Prediction Models That Learn to Avoid Missing Values
Lena Stempfle, Anton Matsson, Newton Mwai +1
Handling missing values at test time is challenging for machine learning models, especially when aiming for both high accuracy and interpretability. Established approaches often ad…