3 papers
cs.LG2025
Bayesian Analysis of Combinatorial Gaussian Process Bandits
Jack Sandberg, Niklas à kerblom, Morteza Haghir Chehreghani
We consider the combinatorial volatile Gaussian process (GP) semi-bandit problem. Each round, an agent is provided a set of available base arms and must select a subset of them to…
cs.LG2025
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Arman Rahbar, Niklas à kerblom, Morteza Haghir Chehreghani
Online decision making plays a crucial role in numerous real-world applications. In many scenarios, the decision is made based on performing a sequence of tests on the incoming dat…
cs.LG2024
Tree Ensembles for Contextual Bandits
Hannes Nilsson, Rikard Johansson, Niklas à kerblom +1
We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Samp…