9 citations · 26 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles
Niklas Åkerblom, Morteza Haghir Chehreghani
In this work, we address the problem of long-distance navigation for battery electric vehicles (BEVs), where one or more charging sessions are required to reach the intended destin…
Online Learning Models for Vehicle Usage Prediction During COVID-19
Tobias Lindroth, Axel Svensson, Niklas Åkerblom +2
Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BE…
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification
Fazeleh Hoseini, Niklas Åkerblom, Morteza Haghir Chehreghani
Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning…
Passive and Active Learning of Driver Behavior from Electric Vehicles
Federica Comuni, Christopher Mészáros, Niklas Åkerblom +1
Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive dri…
Online Learning of Energy Consumption for Navigation of Electric Vehicles
Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
Energy efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consum…
Online Learning of Network Bottlenecks via Minimax Paths
Niklas Åkerblom, Fazeleh Sadat Hoseini, Morteza Haghir Chehreghani
In this paper, we study bottleneck identification in networks via extracting minimax paths. Many real-world networks have stochastic weights for which full knowledge is not availab…
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consum…