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Researcher
Laurens Devos
- Disciplines:Artificial intelligence
Affiliations
- Declarative Languages and Artificial Intelligence (DTAI) (Division)
Member
From1 Aug 2020 → Today - Informatics Section (Division)
Member
From1 Sep 2018 → 4 Aug 2020
Projects
1 - 1 of 1
- Scalable, interpretable and versatile models of relational data: design, induction and inference.From21 Sep 2018 → 31 Oct 2023Funding: FWO Strategic Basic Research Grant
Publications
1 - 9 of 9
- Robustness Verification of Multiclass Tree Ensembles(2024)
Authors: Laurens Devos, Lorenzo Cascioli, Jesse Davis
Pages: 21019 - 21028 - Decision trees: from efficient prediction to responsible AI(2023)
Authors: Hendrik Blockeel, Laurens Devos
- Bitpaths: compressing datasets without decreasing predictive performance(2023)
Authors: Loren Nuyts, Laurens Devos, Wannes Meert, Jesse Davis
Pages: 261 - 268Number of pages: 8 - Detecting Evasion Attacks in Deployed Tree Ensembles(2023)
Authors: Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis
Pages: 120 - 136 - Evaluating Sports Analytics Models: Challenges, Approaches, and Lessons Learned(2022)
Authors: Jesse Davis, Laurens Devos, Wannes Meert, Pieter Robberechts, Jan Van Haaren, Maaike Van Roy
Pages: 1 - 11 - Versatile Verification of Tree Ensembles(2021)
Authors: Laurens Devos, Wannes Meert, Jesse Davis
Pages: 2654 - 2664 - Verifying tree ensembles by reasoning about potential instances(2021)
Authors: Laurens Devos, Wannes Meert, Jesse Davis
Pages: 1 - 9 - Gradient boosting for quantitative finance(2021)
Authors: Jesse Davis, Laurens Devos, Sofie Reyners, Wim Schoutens
Pages: 1 - 40 - Fast gradient boosting decision trees with bit-level data structures(2020)
Authors: Laurens Devos, Wannes Meert, Jesse Davis
Pages: 590 - 606