Samuel Kolb

According to our database, Samuel Kolb authored at least 24 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Top-Down Knowledge Compilation for Counting Modulo Theories.

Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb, Paolo Morettin

CoRR, 2023

Learning MAX-SAT from contextual examples for combinatorial optimisation.

Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt

Artif. Intell., 2023

2022

Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation.

Senne Berden, Mohit Kumar, Samuel Kolb, Tias Guns

Proceedings of the 28th International Conference on Principles and Practice of Constraint Programming, 2022

Learning Constraint Programming Models from Data Using Generate-And-Aggregate.

Mohit Kumar, Samuel Kolb, Tias Guns

Proceedings of the 28th International Conference on Principles and Practice of Constraint Programming, 2022

Human-Machine Collaboration for Democratizing Data Science.

Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt

Proceedings of the Human-Like Machine Intelligence., 2022

2021

Learning Mixed-Integer Linear Programs from Contextual Examples.

Mohit Kumar, Samuel Kolb, Luc De Raedt, Stefano Teso

CoRR, 2021

Optimization of the Patient Flow in a Forensic Psychiatric Hospital with Discrete Event Simulation.

Samuel Kolb, Harold Tiemessen, Peter Wermuth, Jörg Forrer

Proceedings of the Operations Research Proceedings 2021, Selected Papers of the International Conference of the Swiss, German and Austrian Operations Research Societies (SVOR/ASRO, GOR e.V., ÖGOR), University of Bern, Switzerland, August 31, 2021

Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey.

Paolo Morettin, Pedro Zuidberg Dos Martires, Samuel Kolb, Andrea Passerini

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Democratizing Constraint Satisfaction Problems through Machine Learning.

Mohit Kumar, Samuel Kolb, Clément Gautrais, Luc De Raedt

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Predictive spreadsheet autocompletion with constraints.

Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt

Mach. Learn., 2020

Human-Machine Collaboration for Democratizing Data Science.

Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt

CoRR, 2020

Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration.

Pedro Zuidberg Dos Martires, Samuel Kolb

CoRR, 2020

Ordering Variables for Weighted Model Integration.

Vincent Derkinderen, Evert Heylen, Pedro Zuidberg Dos Martires, Samuel Kolb, Luc De Raedt

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

VisualSynth: Democratizing Data Science in Spreadsheets.

Clément Gautrais, Yann Dauxais, Samuel Kolb, Arcchit Jain, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

Learning Weighted Model Integration Distributions.

Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

How to Exploit Structure while Solving Weighted Model Integration Problems.

Samuel Kolb, Pedro Zuidberg Dos Martires, Luc De Raedt

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract).

Yann Dauxais, Clément Gautrais, Anton Dries, Arcchit Jain, Samuel Kolb, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration.

Samuel Kolb, Paolo Morettin, Pedro Zuidberg Dos Martires, Francesco Sommavilla, Andrea Passerini, Roberto Sebastiani, Luc De Raedt

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Learning Linear Programs from Data.

Elias Arnold Schede, Samuel Kolb, Stefano Teso

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

2018

Learning SMT(LRA) Constraints using SMT Solvers.

Samuel Kolb, Stefano Teso, Andrea Passerini, Luc De Raedt

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Efficient Symbolic Integration for Probabilistic Inference.

Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Elements of an Automatic Data Scientist.

Luc De Raedt, Hendrik Blockeel, Samuel Kolb, Stefano Teso, Gust Verbruggen

Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

2017

Learning constraints in spreadsheets and tabular data.

Samuel Kolb, Sergey Paramonov, Tias Guns, Luc De Raedt

Mach. Learn., 2017

TaCLe: Learning Constraints in Tabular Data.

Sergey Paramonov, Samuel Kolb, Tias Guns, Luc De Raedt

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017