activity
20172026
most citedFleetwide data-enabled reliability improvement of wind turbines

44 citations · 98 across the 11 of their papers we have counts for

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Showing cs.LGShow all

9 papers · 1 filter

cs.LG2026

Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles

Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2

Accurate production forecasts are essential for the integration of renewable energy sources into the power grid. This paper illustrates how to obtain probabilistic forecasts of win…

cs.LG2023

Evaluating COVID-19 vaccine allocation policies using Bayesian -top exploration

Alexandra Cimpean, Timothy Verstraeten, Lander Willem +3

Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, in silico. As individual-based models a…

cs.LG2021★ 7 cited

Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making

Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers +2

In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected ut…

cs.LG2021

Scalable Optimization for Wind Farm Control using Coordination Graphs

Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi +3

Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict…

cs.LG2020

Deep reinforcement learning for large-scale epidemic control

Pieter Libin, Arno Moonens, Timothy Verstraeten +4

Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epid…

cs.LG2020★ 3 cited

Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers +1

We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allo…

cs.LG2019★ 1 cited

Fleet Control using Coregionalized Gaussian Process Policy Iteration

Timothy Verstraeten, Pieter JK Libin, Ann Nowé

In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet…

cs.LG2019★ 17 cited

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

Timothy Verstraeten, Eugenio Bargiacchi, Pieter JK Libin +3

Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this r…

cs.LG2017

Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pieter Libin, Timothy Verstraeten, Diederik M. Roijers +4

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies th…