Arkadiy Dushatskiy, Tanja Alderliesten, Peter A.N. Bosman (2021), A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning, In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference p.583-591, Association for Computing Machinery (ACM).

Kaan Yilmaz, Neil Yorke-Smith (2021), A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP, In AI Volume 2 p.150-178.

Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, David Flynn (2021), Baylime: Bayesian local interpretable model-agnostic explanations, Cassio de Campos, Marloes H. Maathuis (Eds.), In Uncertainty in Artificial Intelligence, 27-30 July 2021, Online Volume 161 p.887-896.

Koos van der Linden, Natalia Romero, Mathijs de Weerdt (2021), Benchmarking Flexible Electric Loads Scheduling Algorithms, In Energies Volume 14 p.1-16.

Laurens Bliek, Arthur Guijt, Sicco Verwer, Mathijs De Weerdt (2021), Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints, In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion p.1851-1859, Association for Computing Machinery (ACM).

Georgios Andreadis, Fabian Mastenbroek Mastenbroek, Vincent van Beek, Alexandru Iosup (2021), Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters using Portfolios of Scenarios, In IEEE Transactions on Parallel and Distributed Systems Volume 33 p.26-39.

Frits de Nijs, Erwin Walraven, Mathijs M. de Weerdt, Matthijs T.J. Spaan (2021), Constrained multiagent Markov decision processes: A taxonomy of problems and algorithms, In Journal of Artificial Intelligence Research Volume 70 p.955-1001.

Rickard Karlsson, Laurens Bliek, Sicco Verwer, Mathijs de Weerdt (2021), Continuous Surrogate-Based Optimization Algorithms Are Well-Suited for Expensive Discrete Problems, Mitra Baratchi, Lu Cao, Walter A. Kosters, Jefrey Lijffijt, Jan N. van Rijn, Frank W. Takes (Eds.), In Artificial Intelligence and Machine Learning - 32nd Benelux Conference, BNAIC/Benelearn 2020, Revised Selected Papers p.48-63, Springer.

Ioannis Antonopoulos, Valentin Robu, Benoit Couraud, David Flynn (2021), Data-driven modelling of energy demand response behaviour based on a large-scale residential trial, In Energy and AI Volume 4.

Anna Stawska, Natalia Romero Lane, Mathijs de Weerdt, Remco Verzijlbergh (2021), Demand response: For congestion management or for grid balancing?, In Energy Policy Volume 148.