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Found 10 papers, 0 papers with code
Tree Ensembles for Contextual Bandits
We propose a novel framework for contextual multi-armed bandits based on tree ensembles.
Combinatorial Gaussian Process Bandits in Bayesian Settings: Theory and Application for Energy-Efficient Navigation
We consider a combinatorial Gaussian process semi-bandit problem with time-varying arm availability.
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
In this paper, we provide a novel formulation of the online decision making problem based on combinatorial multi-armed bandits and take the (possibly stochastic) cost of performing tests into account.
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles
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 destination.
Online Learning Models for Vehicle Usage Prediction During COVID-19
One way to mitigate these issues is by performing battery thermal preconditioning, which increases the energy efficiency of the battery.
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification
Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified.
Passive and Active Learning of Driver Behavior from Electric Vehicles
To address the first challenge, passive learning of driver behavior, we investigate non-recurrent architectures such as self-attention models and convolutional neural networks with joint recurrence plots (JRP), and compare them with recurrent models.
Online Learning of Energy Consumption for Navigation of Electric Vehicles
In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound.
Online Learning of Network Bottlenecks via Minimax Paths
In this paper, we study bottleneck identification in networks via extracting minimax paths.
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound.
