4 papers
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,…
Latent Order Bandits
Emil Carlsson, Newton Mwai, Fredrik D. Johansson
Bandit algorithms solve diverse sequential decision-making problems, but are often too sample-inefficient for from-scratch personalization. To substantially reduce exploration time…
Learning Contextual Runtime Monitors for Safe AI-Based Autonomy
Alejandro Luque-Cerpa, Mengyuan Wang, Emil Carlsson +3
We introduce a novel framework for learning context-aware runtime monitors for AI-based control ensembles. Machine-learning (ML) controllers are increasingly deployed in (autonomou…
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…