Kim van den Houten, David M.J. Tax, Esteban Freydell, Mathijs de Weerdt (2024), Learning from Scenarios for Repairable Stochastic Scheduling, Bistra Dilkina (Eds.), In Integration of Constraint Programming, Artificial Intelligence, and Operations Research - 21st International Conference, CPAIOR 2024, Proceedings p.234-242, Springer.

M.B. Elgersma, M.M. de Weerdt, K.I. Aardal, Germán Morales-España, Niina Helistö, Juha Kiviluoma (2024), Online Companion for Tight MIP Formulations for Optimal Operation and Investment of Storage Including Reserves.

Konstantin Sidorov, Gonçalo Homem de Almeida Correia, Mathijs de Weerdt, Emir Demirović (2024), Paths, Proofs, and Perfection: Developing a Human-Interpretable Proof System for Constrained Shortest Paths, In Proceedings of the AAAI Conference on Artificial Intelligence p.20794-20802.

Willem van Jaarsveld, Laurens Bliek, Mathijs de Weerdt, Stella Kapodistria, Verus Pronk, Peter Verleijsdonk, Simon Voorberg, Sicco Verwer, Yingqian Zhang, More Authors (2024), Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration, Boudewijn R. Haverkort, Aldert de Jongste, Pieter van Kuilenburg, Ruben D. Vromans (Eds.), In Commit2Data, Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing.

Issa K. Hanou, Devin Wild Thomas, Wheeler Ruml, Mathijs de Weerdt (2024), Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub, Sara Bernardini, Christian Muise (Eds.), In Proceedings of the 34th International Conference on Automated Planning and Scheduling, ICAPS 2024 p.258-266, Association for the Advancement of Artificial Intelligence (AAAI).

Junhan Wen, Camiel R. Verschoor, Chengming Feng, Irina Mona Epure, Thomas Abeel, Mathijs De Weerdt (2024), The Growing Strawberries Dataset: Tracking Multiple Objects with Biological Development over an Extended Period, In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 p.7089-7099, IEEE.

Grigorii Veviurko, Wendelin Böhmer, Mathijs de Weerdt (2024), To the Max: Reinventing Reward in Reinforcement Learning, In Proceedings of Machine Learning Research p.49455-49470.

Laurens Bliek, Arthur Guijt, Rickard Karlsson, Sicco Verwer, Mathijs de Weerdt (2023), Benchmarking surrogate-based optimisation algorithms on expensive black-box functions, In Applied Soft Computing Volume 147.

Issa K. Hanou, Mathijs M. de Weerdt, Jesse Mulderij (2023), Moving Trains like Pebbles: A Feasibility Study on Tree Yards, In Proceedings International Conference on Automated Planning and Scheduling, ICAPS p.482-490.

J.G.M. van der Linden, M.M. de Weerdt, E. Demirović (2023), Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming, A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, S. Levine (Eds.), In Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Volume 36 p.9173-9212, Curran Associates, Inc..