TaCLe: Learning Constraints in Tabular Data. - Sergey Paramonov 001; Samuel Kolb; Tias Guns; Luc De Raedt (CIKM, 2017)

Learning constraints in spreadsheets and tabular data. - Samuel Kolb; Sergey Paramonov 001; Tias Guns; Luc De Raedt (Mach. Learn., 2017)

Elements of an Automatic Data Scientist. - Luc De Raedt; Hendrik Blockeel; Samuel Kolb; Stefano Teso; Gust Verbruggen (IDA, 2018)

Efficient Symbolic Integration for Probabilistic Inference. - Samuel Kolb; Martin Mladenov; Scott Sanner; Vaishak Belle; Kristian Kersting (IJCAI, 2018)

Learning SMT(LRA) Constraints using SMT Solvers. - Samuel Kolb; Stefano Teso; Andrea Passerini; Luc De Raedt (IJCAI, 2018)

Learning Linear Programs from Data. - Elias Arnold Schede; Samuel Kolb; Stefano Teso (ICTAI, 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 (IJCAI, 2019)

SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract). - Yann Dauxais; Clément Gautrais; Anton Dries; Arcchit Jain; Samuel Kolb; Mohit Kumar 003; Stefano Teso; Elia Van Wolputte; Gust Verbruggen; Luc De Raedt (PKDD/ECML Workshops (1), 2019)

How to Exploit Structure while Solving Weighted Model Integration Problems. - Samuel Kolb; Pedro Zuidberg Dos Martires; Luc De Raedt (UAI, 2019)

Learning Weighted Model Integration Distributions. - Paolo Morettin; Samuel Kolb; Stefano Teso; Andrea Passerini (AAAI, 2020)

VisualSynth: Democratizing Data Science in Spreadsheets. - Clément Gautrais; Yann Dauxais; Samuel Kolb; Arcchit Jain; Mohit Kumar 003; Stefano Teso; Elia Van Wolputte; Gust Verbruggen; Luc De Raedt (ECML/PKDD (5), 2020)

Ordering Variables for Weighted Model Integration. - Vincent Derkinderen; Evert Heylen; Pedro Zuidberg Dos Martires; Samuel Kolb; Luc De Raedt (UAI, 2020)

Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration. - Pedro Zuidberg Dos Martires; Samuel Kolb (CoRR, 2020)

Human-Machine Collaboration for Democratizing Data Science. - Clément Gautrais; Yann Dauxais; Stefano Teso; Samuel Kolb; Gust Verbruggen; Luc De Raedt (CoRR, 2020)

Predictive spreadsheet autocompletion with constraints. - Samuel Kolb; Stefano Teso; Anton Dries; Luc De Raedt (Mach. Learn., 2020)

Democratizing Constraint Satisfaction Problems through Machine Learning. - Mohit Kumar 003; Samuel Kolb; Clément Gautrais; Luc De Raedt (AAAI, 2021)

Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey. - Paolo Morettin; Pedro Zuidberg Dos Martires; Samuel Kolb; Andrea Passerini (IJCAI, 2021)

Optimization of the Patient Flow in a Forensic Psychiatric Hospital with Discrete Event Simulation. - Samuel Kolb; Harold Tiemessen; Peter Wermuth; Jörg Forrer (OR, 2021)

Learning Mixed-Integer Linear Programs from Contextual Examples. - Mohit Kumar 003; Samuel Kolb; Luc De Raedt; Stefano Teso (CoRR, 2021)

Human-Machine Collaboration for Democratizing Data Science. - Clément Gautrais; Yann Dauxais; Stefano Teso; Samuel Kolb; Gust Verbruggen; Luc De Raedt (Human-Like Machine Intelligence, 2022)

Learning Constraint Programming Models from Data Using Generate-And-Aggregate. - Mohit Kumar 003; Samuel Kolb; Tias Guns (CP, 2022)

Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation. - Senne Berden; Mohit Kumar 003; Samuel Kolb; Tias Guns (CP, 2022)

Learning MAX-SAT from contextual examples for combinatorial optimisation. - Mohit Kumar 003; Samuel Kolb; Stefano Teso; Luc De Raedt (Artif. Intell., 2023)

Top-Down Knowledge Compilation for Counting Modulo Theories. - Vincent Derkinderen; Pedro Zuidberg Dos Martires; Samuel Kolb; Paolo Morettin (CoRR, 2023)