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Tias Guns
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- affiliation: Catholic University of Leuven, Belgium
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2020 – today
- 2024
- [j20]Rocsildes Canoy
, Victor Bucarey
Probability estimation and structured output prediction for learning preferences in last mile delivery. Comput. Ind. Eng. 189: 109932 (2024) - [j19]Maxime Mulamba, Jayanta Mandi, Ali Irfan Mahmutogullari, Tias Guns:
Perception-based constraint solving for sudoku images. Constraints An Int. J. 29(1): 112-151 (2024) - [j18]Jayanta Mandi
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities. J. Artif. Intell. Res. 80: 1623-1701 (2024) - [c58]Dimosthenis C. Tsouros, Senne Berden, Tias Guns
Learning to Learn in Interactive Constraint Acquisition. AAAI 2024: 8154-8162 - [c57]Kostis Michailidis
Constraint Modelling with LLMs Using In-Context Learning. CP 2024: 20:1-20:27 - [c56]Wout Vanroose, Ignace Bleukx, Jo Devriendt, Dimos Tsouros, Hélène Verhaeghe
Mutational Fuzz Testing for Constraint Modeling Systems. CP 2024: 29:1-29:25 - [c55]Bastián Véjar, Gaël Aglin, Ali Irfan Mahmutogullari, Siegfried Nijssen, Pierre Schaus, Tias Guns
An Efficient Structured Perceptron for NP-Hard Combinatorial Optimization Problems. CPAIOR (2) 2024: 253-262 - [c54]Jayanta Mandi, Marco Foschini, Daniel Höller, Sylvie Thiébaux, Jörg Hoffmann, Tias Guns:
Decision-Focused Learning to Predict Action Costs for Planning. ECAI 2024: 4060-4067 - [i27]Jayanta Mandi, Marco Foschini, Daniel Höller, Sylvie Thiébaux, Jörg Hoffmann, Tias Guns:
Decision-Focused Learning to Predict Action Costs for Planning. CoRR abs/2408.06876 (2024) - 2023
- [j17]Rocsildes Canoy
Learn and route: learning implicit preferences for vehicle routing. Constraints An Int. J. 28(3): 363-396 (2023) - [j16]Emilio Gamba
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization. J. Artif. Intell. Res. 78: 709-746 (2023) - [c53]Tias Guns
Sudoku Assistant - an AI-Powered App to Help Solve Pen-and-Paper Sudokus. AAAI 2023: 16440-16442 - [c52]Ignace Bleukx, Jo Devriendt, Emilio Gamba
Simplifying Step-Wise Explanation Sequences. CP 2023: 11:1-11:20 - [c51]Dimosthenis C. Tsouros
Guided Bottom-Up Interactive Constraint Acquisition. CP 2023: 36:1-36:20 - [c50]Léonard Tschora, Tias Guns, Erwan Pierre, Marc Plantevit, Céline Robardet:
Electricity Price Forecasting based on Order Books: a differentiable optimization approach. DSAA 2023: 1-10 - [e6]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13713, Springer 2023, ISBN 978-3-031-26386-6 [contents] - [e5]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13714, Springer 2023, ISBN 978-3-031-26389-7 [contents] - [e4]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13715, Springer 2023, ISBN 978-3-031-26408-5 [contents] - [e3]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part IV. Lecture Notes in Computer Science 13716, Springer 2023, ISBN 978-3-031-26411-5 [contents] - [e2]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V. Lecture Notes in Computer Science 13717, Springer 2023, ISBN 978-3-031-26418-4 [contents] - [e1]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part VI. Lecture Notes in Computer Science 13718, Springer 2023, ISBN 978-3-031-26421-4 [contents] - [i26]Jonas Witt, Stef Rasing, Sebastijan Dumancic, Tias Guns, Claus-Christian Carbon:
A Divide-Align-Conquer Strategy for Program Synthesis. CoRR abs/2301.03094 (2023) - [i25]Emilio Gamba, Bart Bogaerts, Tias Guns:
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples). CoRR abs/2303.11712 (2023) - [i24]Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali Irfan Mahmutogullari, Maxime Mulamba, Allegra De Filippo, Tias Guns, Michele Lombardi:
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning. CoRR abs/2307.05213 (2023) - [i23]Dimosthenis C. Tsouros, Senne Berden, Tias Guns:
Guided Bottom-Up Interactive Constraint Acquisition. CoRR abs/2307.06126 (2023) - [i22]Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, Ferdinando Fioretto:
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities. CoRR abs/2307.13565 (2023) - [i21]Dimosthenis C. Tsouros, Hélène Verhaeghe
Holy Grail 2.0: From Natural Language to Constraint Models. CoRR abs/2308.01589 (2023) - [i20]Dimos Tsouros, Senne Berden, Tias Guns:
Learning to Learn in Interactive Constraint Acquisition. CoRR abs/2312.10795 (2023) - 2022
- [j15]Lize Coenen
Machine learning methods for short-term probability of default: A comparison of classification, regression and ranking methods. J. Oper. Res. Soc. 73(1): 191-206 (2022) - [j14]George Petrides
Cost-sensitive learning for profit-driven credit scoring. J. Oper. Res. Soc. 73(2): 338-350 (2022) - [j13]Floris Devriendt
Learning to Rank for Uplift Modeling. IEEE Trans. Knowl. Data Eng. 34(10): 4888-4904 (2022) - [j12]Ligia Maria Moreira Zorello
Baseband-Function Placement With Multi-Task Traffic Prediction for 5G Radio Access Networks. IEEE Trans. Netw. Serv. Manag. 19(4): 5104-5119 (2022) - [c49]Senne Berden, Mohit Kumar, Samuel Kolb, Tias Guns
Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation. CP 2022: 8:1-8:17 - [c48]Mohit Kumar, Samuel Kolb, Tias Guns
Learning Constraint Programming Models from Data Using Generate-And-Aggregate. CP 2022: 29:1-29:16 - [c47]Ignace Bleukx, Senne Berden, Lize Coenen, Nicholas Decleyre, Tias Guns
Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions. CPAIOR 2022: 64-73 - [c46]Jayanta Mandi, Víctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns:
Decision-Focused Learning: Through the Lens of Learning to Rank. ICML 2022: 14935-14947 - [c45]Tias Guns:
From Inductive Databases to a Modeling Language for Pattern Mining, to a Next-Generation Library for Constraint Solving (extended abstract). KDID 2022: 47-50 - [i19]Rocsildes Canoy, Victor Bucarey, Yves Molenbruch, Maxime Mulamba, Jayanta Mandi, Tias Guns:
Probability estimation and structured output prediction for learning preferences in last mile delivery. CoRR abs/2201.10269 (2022) - [i18]Stefano Teso
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens. CoRR abs/2205.10157 (2022) - 2021
- [j11]Bart Bogaerts
A framework for step-wise explaining how to solve constraint satisfaction problems. Artif. Intell. 300: 103550 (2021) - [j10]Jente Van Belle
Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains. Eur. J. Oper. Res. 288(2): 466-479 (2021) - [c44]Sebastijan Dumancic, Tias Guns, Andrew Cropper:
Knowledge Refactoring for Inductive Program Synthesis. AAAI 2021: 7271-7278 - [c43]Jayanta Mandi
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP. CP 2021: 42:1-42:17 - [c42]Gregory Martin, Matthieu Donain, Élisa Fromont, Tias Guns
Prediction-Based Fleet Relocation for Free Floating Car Sharing Services. ICTAI 2021: 1187-1191 - [c41]Emilio Gamba
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization. IJCAI 2021: 1381-1388 - [c40]Maxime Mulamba
Contrastive Losses and Solution Caching for Predict-and-Optimize. IJCAI 2021: 2833-2840 - [i17]Rocsildes Canoy, Victor Bucarey, Jayanta Mandi, Tias Guns:
Learn-n-Route: Learning implicit preferences for vehicle routing. CoRR abs/2101.03936 (2021) - [i16]Emilio Gamba, Bart Bogaerts, Tias Guns:
Efficiently Explaining CSPs with Unsatisfiable Subset Optimization. CoRR abs/2105.11763 (2021) - [i15]Jayanta Mandi, Rocsildes Canoy, Víctor Bucarey, Tias Guns:
Data Driven VRP: A Neural Network Model to Learn Hidden Preferences for VRP. CoRR abs/2108.04578 (2021) - [i14]Jayanta Mandi, Víctor Bucarey, Maxime Mulamba, Tias Guns:
Predict and Optimize: Through the Lens of Learning to Rank. CoRR abs/2112.03609 (2021) - 2020
- [c39]Emir Demirovic, Peter J. Stuckey, Tias Guns
Dynamic Programming for Predict+Optimise. AAAI 2020: 1444-1451 - [c38]Jayanta Mandi, Emir Demirovic, Peter J. Stuckey, Tias Guns
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems. AAAI 2020: 1603-1610 - [c37]Maxime Mulamba
Hybrid Classification and Reasoning for Image-Based Constraint Solving. CPAIOR 2020: 364-380 - [c36]Lize Coenen
Probability of default estimation, with a reject option. DSAA 2020: 439-448 - [c35]Gökberk Koçak
Exploiting Incomparability in Solution Dominance: Improving General Purpose Constraint-Based Mining. ECAI 2020: 331-338 - [c34]Bart Bogaerts
Step-Wise Explanations of Constraint Satisfaction Problems. ECAI 2020: 640-647 - [c33]Jayanta Mandi, Tias Guns:
Interior Point Solving for LP-based prediction+optimisation. NeurIPS 2020 - [i13]Floris Devriendt, Tias Guns, Wouter Verbeke:
Learning to rank for uplift modeling. CoRR abs/2002.05897 (2020) - [i12]Maxime Mulamba, Jayanta Mandi, Rocsildes Canoy, Tias Guns:
Hybrid Classification and Reasoning for Image-based Constraint Solving. CoRR abs/2003.11001 (2020) - [i11]Bart Bogaerts, Emilio Gamba, Tias Guns:
A framework for step-wise explaining how to solve constraint satisfaction problems. CoRR abs/2006.06343 (2020) - [i10]Jayanta Mandi, Tias Guns:
Interior Point Solving for LP-based prediction+optimisation. CoRR abs/2010.13943 (2020) - [i9]Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns:
Discrete solution pools and noise-contrastive estimation for predict-and-optimize. CoRR abs/2011.05354 (2020)
2010 – 2019
- 2019
- [j9]Sheida Hadavi
Monitoring Urban-Freight Transport Based on GPS Trajectories of Heavy-Goods Vehicles. IEEE Trans. Intell. Transp. Syst. 20(10): 3747-3758 (2019) - [c32]Rocsildes Canoy, Tias Guns:
Vehicle Routing by Learning from Historical Solutions. BNAIC/BENELEARN 2019 - [c31]Jens Claes, Bart Bogaerts, Rocsildes Canoy, Emilio Gamba, Tias Guns:
ZebraTutor: Explaining How to Solve Logic Grid Puzzles. BNAIC/BENELEARN 2019 - [c30]Rocsildes Canoy
Vehicle Routing by Learning from Historical Solutions. CP 2019: 54-70 - [c29]Emir Demirovic, Peter J. Stuckey, James Bailey, Jeffrey Chan
An Investigation into Prediction + Optimisation for the Knapsack Problem. CPAIOR 2019: 241-257 - [c28]Emir Demirovic, Peter J. Stuckey, James Bailey, Jeffrey Chan, Christopher Leckie, Kotagiri Ramamohanarao, Tias Guns
Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions. IJCAI 2019: 1078-1085 - [c27]Sebastijan Dumancic, Tias Guns
Learning Relational Representations with Auto-encoding Logic Programs. IJCAI 2019: 6081-6087 - [i8]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. CoRR abs/1903.12577 (2019) - [i7]Rocsildes Canoy, Tias Guns:
Vehicle routing by learning from historical solutions. CoRR abs/1909.07893 (2019) - [i6]Gökberk Koçak, Özgür Akgün, Tias Guns, Ian Miguel:
Towards Improving Solution Dominance with Incomparability Conditions: A case-study using Generator Itemset Mining. CoRR abs/1910.00505 (2019) - [i5]Jayanta Mandi
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems. CoRR abs/1911.10092 (2019) - 2018
- [c26]John O. R. Aoga
Finding Probabilistic Rule Lists using the Minimum Description Length Principle. DS 2018: 66-82 - [i4]Tias Guns, Peter J. Stuckey, Guido Tack:
Solution Dominance over Constraint Satisfaction Problems. CoRR abs/1812.09207 (2018) - 2017
- [j8]Andrea Passerini
Introduction to the special issue on Combining Constraint Solving with Mining and Learning. Artif. Intell. 244: 1-5 (2017) - [j7]Tias Guns
MiningZinc: A declarative framework for constraint-based mining. Artif. Intell. 244: 6-29 (2017) - [j6]John O. R. Aoga
Mining Time-constrained Sequential Patterns with Constraint Programming. Constraints An Int. J. 22(4): 548-570 (2017) - [j5]Christian Bessiere, Luc De Raedt
The Inductive Constraint Programming Loop. IEEE Intell. Syst. 32(5): 44-52 (2017) - [j4]Samuel Kolb, Sergey Paramonov
Learning constraints in spreadsheets and tabular data. Mach. Learn. 106(9-10): 1441-1468 (2017) - [c25]Sergey Paramonov, Samuel Kolb, Tias Guns
TaCLe: Learning Constraints in Tabular Data. CIKM 2017: 2511-2514 - [c24]Pierre Schaus, John O. R. Aoga
CoverSize: A Global Constraint for Frequency-Based Itemset Mining. CP 2017: 529-546 - [c23]Behrouz Babaki
Stochastic Constraint Programming with And-Or Branch-and-Bound. IJCAI 2017: 539-545 - [c22]Sergey Paramonov, Tao Chen, Tias Guns:
Generic Mining of Condensed Pattern Representations under Constraints. YSIP 2017: 168-177 - 2016
- [c21]Tias Guns, Sergey Paramonov, Benjamin Négrevergne:
On Declarative Modeling of Structured Pattern Mining. AAAI Workshop: Declarative Learning Based Programming 2016 - [c20]Tias Guns
Repetitive Branch-and-Bound Using Constraint Programming for Constrained Minimum Sum-of-Squares Clustering. ECAI 2016: 462-470 - [c19]Tias Guns:
Towards generic and efficient constraint-based mining, a constraint programming approach. EGC 2016: 13-20 - [c18]Tias Guns, Achille Aknin, Jefrey Lijffijt
Direct Mining of Subjectively Interesting Relational Patterns. ICDM 2016: 913-918 - [c17]John O. R. Aoga, Tias Guns
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming. ECML/PKDD (2) 2016: 315-330 - [p5]Luc De Raedt
Learning Constraint Satisfaction Problems: An ILP Perspective. Data Mining and Constraint Programming 2016: 96-112 - [p4]Anton Dries
Modeling in MiningZinc. Data Mining and Constraint Programming 2016: 257-281 - [p3]Valerio Grossi, Tias Guns
Partition-Based Clustering Using Constraint Optimization. Data Mining and Constraint Programming 2016: 282-299 - [p2]Christian Bessiere, Luc De Raedt
The Inductive Constraint Programming Loop. Data Mining and Constraint Programming 2016: 303-309 - [i3]John O. R. Aoga, Tias Guns, Pierre Schaus:
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming. CoRR abs/1604.01166 (2016) - 2015
- [j3]Tias Guns
Declarative pattern mining using constraint programming. Constraints An Int. J. 20(4): 492-493 (2015) - [c16]Andrea Rendl, Tias Guns
MiniSearch: A Solver-Independent Meta-Search Language for MiniZinc. CP 2015: 376-392 - [c15]Benjamin Négrevergne, Tias Guns
Constraint-Based Sequence Mining Using Constraint Programming. CPAIOR 2015: 288-305 - [c14]Behrouz Babaki
Constraint-Based Querying for Bayesian Network Exploration. IDA 2015: 13-24 - [i2]Benjamin Négrevergne, Tias Guns:
Constraint-based sequence mining using constraint programming. CoRR abs/1501.01178 (2015) - [i1]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. CoRR abs/1510.03317 (2015) - 2014
- [c13]Behrouz Babaki
Constrained Clustering Using Column Generation. CPAIOR 2014: 438-454 - 2013
- [j2]Tias Guns
k-Pattern Set Mining under Constraints. IEEE Trans. Knowl. Data Eng. 25(2): 402-418 (2013) - [c12]Benjamin Négrevergne, Anton Dries
Dominance Programming for Itemset Mining. ICDM 2013: 557-566 - [c11]Tias Guns
The MiningZinc Framework for Constraint-Based Itemset Mining. ICDM Workshops 2013: 1081-1084 - [c10]Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt:
MiningZinc: A Modeling Language for Constraint-Based Mining. IJCAI 2013: 1365-1372 - 2012
- [c9]Thanh Le Van, Ana Carolina Fierro, Tias Guns
Mining Local Staircase Patterns in Noisy Data. ICDM Workshops 2012: 139-146 - 2011
- [j1]Tias Guns
Itemset mining: A constraint programming perspective. Artif. Intell. 175(12-13): 1951-1983 (2011) - [c8]Tias Guns
Declarative Heuristic Search for Pattern Set Mining. ICDM Workshops 2011: 1104-1111 - [c7]Siegfried Nijssen, Aída Jiménez, Tias Guns
Constraint-Based Pattern Mining in Multi-relational Databases. ICDM Workshops 2011: 1120-1127 - [c6]Tias Guns, Siegfried Nijssen
Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. PAKDD (2) 2011: 382-394 - 2010
- [c5]Luc De Raedt, Tias Guns, Siegfried Nijssen:
Constraint Programming for Data Mining and Machine Learning. AAAI 2010: 1671-1675 - [c4]Tias Guns
Cis-regulatory module detection using constraint programming. BIBM 2010: 363-368 - [c3]Siegfried Nijssen, Tias Guns
Integrating Constraint Programming and Itemset Mining. ECML/PKDD (2) 2010: 467-482 - [p1]Jérémy Besson, Jean-François Boulicaut, Tias Guns
Generalizing Itemset Mining in a Constraint Programming Setting. Inductive Databases and Constraint-Based Data Mining 2010: 107-126
2000 – 2009
- 2009
- [c2]Siegfried Nijssen
Correlated itemset mining in ROC space: a constraint programming approach. KDD 2009: 647-656 - 2008
- [c1]Luc De Raedt
Constraint programming for itemset mining. KDD 2008: 204-212
Coauthor Index
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