Timothy Verstraeten
Timothy Verstraeten
Doctoral Researcher in Computer Science, Vrije Universiteit Brussel
Geverifieerd e-mailadres voor vub.ac.be
Geciteerd door
Geciteerd door
Learning to coordinate with coordination graphs in repeated single-stage multi-agent decision problems
E Bargiacchi, T Verstraeten, D Roijers, A Nowé, H Hasselt
International conference on machine learning, 482-490, 2018
Bayesian best-arm identification for selecting influenza mitigation strategies
PJK Libin, T Verstraeten, DM Roijers, J Grujic, K Theys, P Lemey, A Nowé
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018
Deep reinforcement learning for large-scale epidemic control
P Libin, A Moonens, T Verstraeten, F Perez-Sanjines, N Hens, P Lemey, ...
arXiv preprint arXiv:2003.13676, 2020
Fleetwide data-enabled reliability improvement of wind turbines
T Verstraeten, A Nowe, J Keller, Y Guo, S Sheng, J Helsen
Renewable and Sustainable Energy Reviews 109, 428-437, 2019
Multi-agent Thompson sampling for bandit applications with sparse neighbourhood structures
T Verstraeten, E Bargiacchi, PJK Libin, J Helsen, DM Roijers, A Nowé
Scientific reports 10 (1), 1-13, 2020
Efficient evaluation of influenza mitigation strategies using preventive bandits
P Libin, T Verstraeten, K Theys, DM Roijers, P Vrancx, A Nowé
International Conference on Autonomous Agents and Multiagent Systems, 67-85, 2017
Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics
PJK Libin, L Willem, T Verstraeten, A Torneri, J Vanderlocht, N Hens
PLoS computational biology 17 (3), e1008688, 2021
Thompson Sampling for m-top Exploration.
P Libin, T Verstraeten, DM Roijers, W Wang, K Theys, A Nowé
Fleet-oriented pattern mining combined with time series signature extraction for understanding of wind farm response to storm conditions
PJ Daems, L Feremans, T Verstraeten, B Cule, B Goethals, J Helsen
Fleet Control using Coregionalized Gaussian Process Policy Iteration
T Verstraeten, PJK Libin, A Nowé
24th European Conference on Artificial Intelligence, 1571-1578, 2020
Bayesian anytime m-top exploration
P Libin, T Verstraeten, DM Roijers, W Wang, K Theys, A Nowe
2019 IEEE 31st International Conference on Tools with Artificial …, 2019
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers
FG Marulanda, P Libin, T Verstraeten, A Nowé
2018 IEEE 30th International Conference on Tools with Artificial …, 2018
Reinforcement learning for fleet applications using coregionalized gaussian processes
T Verstraeten, A Nowé
Adaptive Learning Agents (ALA) Workshop at AAMAS). IFAAMAS 57, 2018
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
R Rădulescu, T Verstraeten, Y Zhang, P Mannion, DM Roijers, A Nowé
arXiv preprint arXiv:2011.07290, 2020
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
E Bargiacchi, T Verstraeten, DM Roijers, A Nowé
arXiv preprint arXiv:2001.07527, 2020
Thompson sampling for loosely-coupled multi-agent systems: An application to wind farm control
T Verstraeten, E Bargiacchi, PJ Libin, J Helsen, DM Roijers, A Nowé
Adaptive and Learning Agents Workshop, 2020
Edge computing for advanced vibration signal processing
T Verstraeten, FG Marulanda, C Peeters, PJ Daems, A Nowé, J Helsen
Surveillance, Vishno and AVE conferences, 2019
Fleet-wide condition monitoring combining vibration signal processing and machine learning rolled out in a cloud-computing environment
J Helsen, C Peeters, T Verstraeten, J Verbeke, N Gioia, A Nowé
International Conference on Noise and Vibration Engineering (ISMA), 2018
Effects of wake on gearbox design load cases identified from fleet-wide operational data
PJ Daems, T Verstraeten, C Peeters, J Helsen
Forschung im Ingenieurwesen, 1-6, 2021
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ...
arXiv preprint arXiv:2103.09568, 2021
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