publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2022

  1. Inductive Logic Programming At 30: A New Introduction
    Andrew Cropper, and Sebastijan Dumancic
    J. Artif. Intell. Res., 2022
  2. SaDe: Learning Models that Provably Satisfy Domain Constraints
    Kshitij Goyal, Sebastijan Dumancic, and Hendrik Blockeel
    ECMLPKDD, 2022

2021

  1. Automated Reasoning and Learning for Automated Payroll Management
    Sebastijan Dumancic, Wannes Meert, Stijn Goethals, Tim Stuyckens, Jelle Huygen, and Koen Denies
    In Innovative Applications of Artificial Intelligence (IAAI), 2021
    PDF
  2. Knowledge Refactoring for Inductive Program Synthesis
    Sebastijan Dumancic, Tias Guns, and Andrew Cropper
    In AAAI Conference on Artificial Intelligence (AAAI), 2021
    PDF
  3. Avatar - Automated Feature Wrangling for Machine Learning
    Gust Verbruggen, Elia Van Wolputte, Sebastijan Dumancic, and Luc De Raedt
    In Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, 2021
    PDF
  4. Neural probabilistic logic programming in DeepProbLog
    Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, and Luc De Raedt
    Artificial Intelligence, 2021
  5. Inductive logic programming at 30
    Andrew Cropper, Sebastijan Dumančić, Richard Evans, and Stephen Muggleton
    Machine Learning, 2021

2020

  1. Learning Large Logic Programs by Going Beyond Entailment
    Andrew Cropper, and Sebastijan Dumancic
    In International Joint Conference on Artificial Intelligence, 2020
    PDF
  2. Turning 30: New Ideas in Inductive Logic Programming
    Andrew Cropper, and Sebastijan Dumancic
    In International Joint Conference on Artificial Intelligence, 2020
    PDF
  3. From Statistical Relational to Neural-Symbolic Artificial Intelligence
    Luc De Raedt, Sebastijan Dumancic, Robin Manhaeve, and Giuseppe Marra
    In International Joint Conference on Artificial Intelligence, 2020
    PDF
  4. Tackling Noise in Semi-Supervised Clustering
    Jonas Soenen, Sebastijan Dumancic, and Hendrik Blockeel
    In European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECML), 2020
    PDF

2019

  1. Learning Relational Representations with Auto-encoding Logic Programs
    Sebastijan Dumancic, Tias Guns, Wannes Meert, and Hendrik Blockeel
    In International Joint Conference on Artificial Intelligence, 2019
    PDF
  2. A comparative study of distributional and symbolic paradigms for relational learning
    Sebastijan Dumancic, Alberto Garcı́a-Durán, and Mathias Niepert
    In International Joint Conference on Artificial Intelligence, 2019
    PDF

2018

  1. On embeddings as an alternative paradigm for relational learning
    Sebastijan Dumancic, Alberto Garcı́a-Durán, and Mathias Niepert
    In 8th workshop on Statistical Relational Artificial Intelligence StarAI at IJCAI/ICML, 2018
    PDF Poster
  2. Auto-encoding Logic Programs
    Sebastijan Dumancic, Tias Guns, Wannes Meert, and Hendrik Blockeel
    In 2nd workshop on Neural Abstract Machines and Program Induction NAMPI at ICML, 2018
    PDF Poster
  3. COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
    Toon van Craenendonck, Sebastijan Dumancic, Elia Van Wolputte, and Hendrik Blockeel
    In Symposium on Intelligent Data Analysis (IDA) 2018, 2018
    PDF Code
  4. COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
    Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, and Hendrik Blockeel
    In Discovery Science, 2018
    PDF Code
  5. DeepProbLog: Neural Probabilistic Logic Programming
    Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt
    In Advances in Neural Information Processing Systems (NIPS), 2018
    PDF
  6. Learning Sequence Encoders for Temporal Knowledge Graph Completion
    Alberto Garcı́a-Durán, Sebastijan Dumancic, and Mathias Niepert
    In Empirical Methods in Natural Language Processing, 2018
    PDF

2017

  1. Demystifying Relational Latent Representations
    Sebastijan Dumancic, and Hendrik Blockeel
    In ILP, 2017
    PDF Code Poster Slides
  2. Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation
    Sebastijan Dumancic, and Hendrik Blockeel
    In IJCAI, 2017
    PDF Code Poster Slides
  3. COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
    Toon van Craenendonck, Sebastijan Dumancic, and Hendrik Blockeel
    In IJCAI, 2017
    PDF Code
  4. An expressive dissimilarity measure for relational clustering using neighbourhood trees
    Sebastijan Dumancic, and Hendrik Blockeel
    Machine Learning, 2017
    PDF Code Slides

2016

  1. Unsupervised Relational Representation Learning via Clustering: Preliminary Results
    Sebastijan Dumancic, and Hendrik Blockeel
    In 6th workshop on Statistical Relational Artificial Intelligence StarAI at IJCAI, 2016
    PDF
  2. Theory reconstruction: a representation learning view on predicate invention
    Sebastijan Dumancic, Wannes Meert, and Hendrik Blockeel
    In 6th workshop on Statistical Relational Artificial Intelligence StarAI at IJCAI, 2016
    PDF

2015

    2014