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Emir Demirović, Ciaran McCreesh, Matthew J. McIlree, Jakob Nordström, Andy Oertel, Konstantin Sidorov (2024), Pseudo-Boolean Reasoning about States and Transitions to Certify Dynamic Programming and Decision Diagram Algorithms, Paul Shaw (Eds.), In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024), Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing.

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.

N.J. Schutte, K.S. Postek, N. Yorke-Smith (2024), Robust Losses for Decision-Focused Learning, Kate Larson (Eds.), In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence p.4868-4875.

Yun Li, Neil Yorke-Smith, Tamas Keviczky (2024), Robust Optimal Control with Binary Adjustable Uncertainties, In Proceedings of the European Control Conference, ECC 2024 p.3721-3727.

Yanqi Qiao, Dazhuang Liu, Rui Wang, Kaitai Liang (2024), Stealthy Backdoor Attack against Federated Learning through Frequency Domain by Backdoor Neuron Constraint and Model Camouflage, In IEEE Journal on Emerging and Selected Topics in Circuits and Systems Volume 14 p.661-672.

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.

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.