3 papers
cs.LG2024
Tree Ensembles for Contextual Bandits
Hannes Nilsson, Rikard Johansson, Niklas Åkerblom +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…
cs.LG2023
Bayesian Analysis of Combinatorial Gaussian Process Bandits
Jack Sandberg, Niklas Åkerblom, 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.LG2023
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Arman Rahbar, Niklas Åkerblom, 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…