no code implementations • 17 Dec 2023 • Dimos Tsouros, Senne Berden, Tias Guns
However, a large number of queries is still required to learn the model, which is a major limitation.
no code implementations • 3 Aug 2023 • Dimos Tsouros, Hélène Verhaeghe, Serdar Kadıoğlu, Tias Guns
Twenty-seven years ago, E. Freuder highlighted that "Constraint programming represents one of the closest approaches computer science has yet made to the Holy Grail of programming: the user states the problem, the computer solves it".
1 code implementation • 25 Jul 2023 • Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, Ferdinando Fioretto
Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains a model to optimize decisions, integrating prediction and optimization in an end-to-end system.
1 code implementation • 12 Jul 2023 • Dimos Tsouros, Senne Berden, Tias Guns
Second, we propose a probability-based method to guide query generation and show that it can significantly reduce the number of queries required to converge.
no code implementations • 11 Jul 2023 • Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali İrfan Mahmutoğulları, Maxime Mulamba, Allegra De Filippo, Tias Guns, Michele Lombardi
Our experiments show that by using SFGE we can: (1) deal with predictions that occur both in the objective function and in the constraints; and (2) effectively tackle two-stage stochastic optimization problems.
no code implementations • 21 Mar 2023 • Emilio Gamba, Bart Bogaerts, Tias Guns
We build on a recently proposed method for stepwise explaining solutions of Constraint Satisfaction Problems (CSP) in a human-understandable way.
no code implementations • 8 Jan 2023 • Jonas Witt, Stef Rasing, Sebastijan Dumančić, Tias Guns, Claus-Christian Carbon
We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems.
no code implementations • 20 May 2022 • Stefano Teso, Laurens Bliek, Andrea Borghesi, Michele Lombardi, Neil Yorke-Smith, Tias Guns, Andrea Passerini
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
no code implementations • 25 Jan 2022 • Rocsildes Canoy, Victor Bucarey, Yves Molenbruch, Maxime Mulamba, Jayanta Mandi, Tias Guns
Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further.
1 code implementation • 7 Dec 2021 • Jayanta Mandi, Víctor Bucarey, Maxime Mulamba, Tias Guns
In the last years decision-focused learning framework, also known as predict-and-optimize, have received increasing attention.
1 code implementation • 10 Aug 2021 • Jayanta Mandi, Rocsildes Canoy, Víctor Bucarey, Tias Guns
These preferences are in the form of arc probabilities, i. e., the more preferred a route is, the higher is the joint probability.
1 code implementation • 25 May 2021 • Emilio Gamba, Bart Bogaerts, Tias Guns
We build on a recently proposed method for explaining solutions of constraint satisfaction problems.
no code implementations • 11 Jan 2021 • Rocsildes Canoy, Víctor Bucarey, Jayanta Mandi, Tias Guns
Even in the case of changes in the customer sets, our method is able to find solutions that are closer to the actual routings than when using only distances, and hence, solutions that require fewer manual changes when transformed into practical routings.
2 code implementations • 10 Nov 2020 • Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns
Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data.
1 code implementation • NeurIPS 2020 • Jayanta Mandi, Tias Guns
Solving optimization problems is the key to decision making in many real-life analytics applications.
no code implementations • 11 Jun 2020 • Bart Bogaerts, Emilio Gamba, Tias Guns
We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP.
1 code implementation • 21 Apr 2020 • Sebastijan Dumancic, Tias Guns, Andrew Cropper
We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it.
1 code implementation • 24 Mar 2020 • Maxime Mulamba, Jayanta Mandi, Rocsildes Canoy, Tias Guns
We explore the trade-off between the power of the classifier and the power of the constraint reasoning, as well as further integration through the additional use of structural knowledge.
no code implementations • 14 Feb 2020 • Floris Devriendt, Tias Guns, Wouter Verbeke
We propose a unified formalisation of different global uplift modeling measures in use today and explore how these can be integrated into the learning-to-rank framework.
1 code implementation • 22 Nov 2019 • Jaynta Mandi, Emir Demirović, Peter J. Stuckey, Tias Guns
Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems.
1 code implementation • 1 Oct 2019 • Gökberk Koçak, Özgür Akgün, Tias Guns, Ian Miguel
In this paper, in addition to specifying a dominance relation, we introduce the ability to specify an incomparability condition.
no code implementations • 17 Sep 2019 • Rocsildes Canoy, Tias Guns
Even in the case of changes in the client sets, our method is able to find solutions that are closer to the actual route plans than when using distances, and hence, solutions that would require fewer manual changes to transform into the actual route plan.
no code implementations • 29 Mar 2019 • Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel
This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks.
no code implementations • 21 Dec 2018 • Tias Guns, Peter J. Stuckey, Guido Tack
We define Constraint Dominance Problems (CDPs) as CSPs with a dominance relation, that is, a preorder over the solutions of the CSP.
no code implementations • 5 Apr 2016 • John O. R. Aoga, Tias Guns, Pierre Schaus
Detailed experiments show how this approach outperforms existing CP as well as specialized systems for SPM, and that the gain in efficiency translates directly into increased efficiency for other settings such as mining with regular expressions.
no code implementations • 12 Oct 2015 • Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.
no code implementations • 6 Jan 2015 • Benjamin Negrevergne, Tias Guns
We investigate the use of constraint programming as general framework for this task.