5 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…
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…
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
Fredrik D. Johansson
Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created…
Active Preference Learning for Ordering Items In- and Out-of-sample
Herman Bergström, Emil Carlsson, Devdatt Dubhashi +1
Learning an ordering of items based on pairwise comparisons is useful when items are difficult to rate consistently on an absolute scale, for example, when annotators have to make…