Projects per year
Fingerprint
Dive into the research topics where Tias Guns is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
-
FWOAL1002: FRESCO: A FRamework for Explainable Solving and Constraint Optimization
1/01/21 → 31/12/24
Project: Fundamental
-
BRGRD51: Joint R&D 2020: " RECONCILE: gReenEr produCtiON sCheduling by automatIcally Learning from Experts"
1/07/20 → 30/06/23
Project: Applied
-
-
SRP56: SRP-Onderzoekszwaartepunt: Autonomous Mobility & Logistics
Van Mierlo, J., Macharis, C., Coosemans, T., Hegazy, O., Van Lier, T., Crispeels, T., Guns, T., Vanhaverbeke, L., Rombaut, E., Berecibar, M., Hesami, S., Arapoglou, S., Li, J. & De Cauwer, C.
1/03/19 → 29/02/24
Project: Fundamental
Research output
-
Decision-Focused Learning: Through the Lens of Learning to Rank
Mandi, J., Bucarey Lopez, V., Mulamba, M. & Guns, T., 17 Jul 2022, Decision-Focused Learning: Through the Lens of Learning to Rank. Baltimore, Maryland, USA: Proceedings of Machine Learning Research, Vol. 162. p. 14935-14947 13 p. (Proceedings of Machine Learning Research; vol. 162).Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access -
Learning to Rank for Uplift Modeling
Devriendt, F., Van Belle, J., Guns, T. & Verbeke, W., 1 Oct 2022, In: IEEE Transactions on Knowledge and Data Engineering. 34, 10, p. 4888-4904 17 p.Research output: Contribution to journal › Article › peer-review
5 Citations (Scopus) -
A framework for step-wise explaining how to solve constraint satisfaction problems
Bogaerts, B., Gamba, E. & Guns, T., Nov 2021, In: Artificial Intelligence. 300, 103550.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (Scopus)35 Downloads (Pure) -
Contrastive Losses and Solution Caching for Predict-and-Optimize
Mulamba, M., Mandi, J., Diligenti, M., Lombardi, M., Bucarey Lopez, V. & Guns, T., 19 Aug 2021, Contrastive Losses and Solution Caching for Predict-and-Optimize. Zhou, Z-H. (ed.). International Joint Conferences on Artificial Intelligence, p. 2833-2840 8 p. (IJCAI International Joint Conference on Artificial Intelligence).Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access1 Citation (Scopus) -
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP
Mandi, J., Canoy, R., Bucarey Lopez, V. & Guns, T., 15 Oct 2021, Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP. Michel, L. D. (ed.). Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Vol. 210. p. 42:1-42:17 17 p. 42. (Leibniz International Proceedings in Informatics (LIPIcs)).Research output: Chapter in Book/Report/Conference proceeding › Conference paper
Open Access
Activities
-
Predict and Optimize: Through the Lens of Learning to Rank
Jayanta Mandi (Speaker), Victor Bucarey Lopez (Speaker), Maxime Albert Mulamba Ke Tchomba (Speaker) & Tias Guns (Speaker)
3 Jul 2022 → 6 Jul 2022Activity: Talk or presentation › Talk or presentation at a conference
-
Interior Point Solving for LP-based prediction+optimisation
Jayanta Mandi (Speaker) & Tias Guns (Speaker)
8 Jan 2021Activity: Talk or presentation › Talk or presentation at a workshop/seminar
File -
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
Emilio Gamba (Speaker), Tias Guns (Contributor) & Bart Bogaerts (Contributor)
Aug 2021Activity: Talk or presentation › Talk or presentation at a conference
File -
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
Emilio Gamba (Speaker), Tias Guns (Contributor) & Bart Bogaerts (Contributor)
21 Sep 2021Activity: Talk or presentation › Talk or presentation at a workshop/seminar
File -
Data Driven VRP: A Neural Network Model to learn hidden preferences for VRP
Jayanta Mandi (Speaker), Rocsildes Canoy (Presenter), Victor Bucarey Lopez (Presenter) & Tias Guns (Presenter)
27 Oct 2021Activity: Talk or presentation › Talk or presentation at a conference