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ORCIDSebastijan Dumancic
Person information
- affiliation: Delft University of Technology, The Netherlands
- affiliation (former): KU Leuven, Belgium
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2020 – today
- 2024
[j6]Giuseppe Marra
, Sebastijan Dumancic, Robin Manhaeve, Luc De Raedt:
From statistical relational to neurosymbolic artificial intelligence: A survey. Artif. Intell. 328: 104062 (2024)- [c24]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
DeepSaDe: Learning Neural Networks That Guarantee Domain Constraint Satisfaction. AAAI 2024: 12199-12207 - [c23]Céline Hocquette, Sebastijan Dumancic, Andrew Cropper:
Learning Logic Programs by Discovering Higher-Order Abstractions. IJCAI 2024: 3421-3429 - [i25]Tilman Hinnerichs, Robin Manhaeve, Giuseppe Marra, Sebastijan Dumancic:
Towards a fully declarative neuro-symbolic language. CoRR abs/2405.09521 (2024) - 2023
- [c22]Dirk van Bokkem, Max van den Hemel, Sebastijan Dumancic, Neil Yorke-Smith:
Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation. AAAI 2023: 15731-15737 - [i24]Jonas Witt, Stef Rasing, Sebastijan Dumancic, Tias Guns, Claus-Christian Carbon:
A Divide-Align-Conquer Strategy for Program Synthesis. CoRR abs/2301.03094 (2023) - [i23]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction. CoRR abs/2303.01141 (2023) - [i22]Céline Hocquette, Sebastijan Dumancic, Andrew Cropper:
Learning Logic Programs by Discovering Higher-Order Abstractions. CoRR abs/2308.08334 (2023) - 2022
- [j5]Andrew Cropper, Sebastijan Dumancic:
Inductive Logic Programming At 30: A New Introduction. J. Artif. Intell. Res. 74: 765-850 (2022) - [j4]Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton:
Inductive logic programming at 30. Mach. Learn. 111(1): 147-172 (2022) - [c21]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
SaDe: Learning Models that Provably Satisfy Domain Constraints. ECML/PKDD (5) 2022: 410-425 - 2021
- [j3]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
Neural probabilistic logic programming in DeepProbLog. Artif. Intell. 298: 103504 (2021) - [c20]Sebastijan Dumancic, Tias Guns, Andrew Cropper:
Knowledge Refactoring for Inductive Program Synthesis. AAAI 2021: 7271-7278 - [c19]Sebastijan Dumancic, Wannes Meert, Stijn Goethals, Tim Stuyckens, Jelle Huygen, Koen Denies:
Automated Reasoning and Learning for Automated Payroll Management. AAAI 2021: 15107-15116 - [c18]Gust Verbruggen, Elia Van Wolputte, Sebastijan Dumancic, Luc De Raedt:
avatar - Automated Feature Wrangling for Machine Learning. IDA 2021: 235-247 - [p1]Robin Manhaeve, Giuseppe Marra, Thomas Demeester, Sebastijan Dumancic, Angelika Kimmig, Luc De Raedt:
Neuro-Symbolic AI = Neural + Logical + Probabilistic AI. Neuro-Symbolic Artificial Intelligence 2021: 173-191 - [i21]Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton:
Inductive logic programming at 30. CoRR abs/2102.10556 (2021) - [i20]Giuseppe Marra, Sebastijan Dumancic, Robin Manhaeve, Luc De Raedt:
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey. CoRR abs/2108.11451 (2021) - [i19]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
SaDe: Learning Models that Provably Satisfy Domain Constraints. CoRR abs/2112.00552 (2021) - 2020
- [j2]Grace Bang, Guy Barash, Ryan Beal, Jacques Calì, Mauricio Castillo-Effen, Xin Cynthia Chen, Niyati Chhaya, Rachel Cummings, Rohan Dhoopar, Sebastijan Dumancic, Huáscar Espinoza, Eitan Farchi, Ferdinando Fioretto, Raquel Fuentetaja, Christopher William Geib, Odd Erik Gundersen, José Hernández-Orallo, Xiaowei Huang, Kokil Jaidka, Sarah Keren, Seokhwan Kim, Michel Galley, Xiaomo Liu, Tyler Lu, Zhiqiang Ma, Richard Mallah, John A. McDermid, Martin Michalowski, Reuth Mirsky, Seán Ó hÉigeartaigh, Deepak Ramachandran, Javier Segovia-Aguas, Onn Shehory, Arash Shaban-Nejad, Vered Shwartz, Siddharth Srivastava, Kartik Talamadupula, Jian Tang, Pascal Van Hentenryck, Dell Zhang, Jian Zhang:
The Association for the Advancement of Artificial Intelligence 2020 Workshop Program. AI Mag. 41(4): 100-114 (2020) - [c17]Andrew Cropper, Sebastijan Dumancic:
Learning Large Logic Programs By Going Beyond Entailment. IJCAI 2020: 2073-2079 - [c16]Andrew Cropper, Sebastijan Dumancic, Stephen H. Muggleton:
Turning 30: New Ideas in Inductive Logic Programming. IJCAI 2020: 4833-4839 - [c15]Luc De Raedt, Sebastijan Dumancic, Robin Manhaeve, Giuseppe Marra:
From Statistical Relational to Neuro-Symbolic Artificial Intelligence. IJCAI 2020: 4943-4950 - [c14]Jonas Soenen, Sebastijan Dumancic, Toon van Craenendonck, Hendrik Blockeel:
Tackling Noise in Active Semi-supervised Clustering. ECML/PKDD (2) 2020: 121-136 - [i18]Andrew Cropper, Sebastijan Dumancic, Stephen H. Muggleton:
Turning 30: New Ideas in Inductive Logic Programming. CoRR abs/2002.11002 (2020) - [i17]Luc De Raedt, Sebastijan Dumancic, Robin Manhaeve, Giuseppe Marra:
From Statistical Relational to Neuro-Symbolic Artificial Intelligence. CoRR abs/2003.08316 (2020) - [i16]Andrew Cropper, Sebastijan Dumancic:
Learning large logic programs by going beyond entailment. CoRR abs/2004.09855 (2020) - [i15]Sebastijan Dumancic, Andrew Cropper:
Knowledge Refactoring for Program Induction. CoRR abs/2004.09931 (2020) - [i14]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
Feature Interactions in XGBoost. CoRR abs/2007.05758 (2020) - [i13]Andrew Cropper, Sebastijan Dumancic:
Inductive logic programming at 30: a new introduction. CoRR abs/2008.07912 (2020)
2010 – 2019
- 2019
- [c13]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. BNAIC/BENELEARN 2019 - [c12]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. IJCAI 2019: 6081-6087 - [c11]Sebastijan Dumancic, Alberto García-Durán, Mathias Niepert:
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning. IJCAI 2019: 6088-6094 - [c10]Luc De Raedt, Robin Manhaeve, Sebastijan Dumancic, Thomas Demeester, Angelika Kimmig:
Neuro-Symbolic = Neural + Logical + Probabilistic. NeSy@IJCAI 2019 - [i12]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. CoRR abs/1903.12577 (2019) - [i11]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. CoRR abs/1907.08194 (2019) - 2018
- [c9]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
COBRASTS: A New Approach to Semi-supervised Clustering of Time Series. DS 2018: 179-193 - [c8]Alberto García-Durán, Sebastijan Dumancic, Mathias Niepert:
Learning Sequence Encoders for Temporal Knowledge Graph Completion. EMNLP 2018: 4816-4821 - [c7]Toon van Craenendonck, Sebastijan Dumancic, Elia Van Wolputte, Hendrik Blockeel:
COBRAS: Interactive Clustering with Pairwise Queries. IDA 2018: 353-366 - [c6]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. NeurIPS 2018: 3753-3763 - [c5]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
Interactive Time Series Clustering with COBRASTS. ECML/PKDD (3) 2018: 654-657 - [i10]Toon van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel:
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints. CoRR abs/1801.09955 (2018) - [i9]Toon van Craenendonck, Sebastijan Dumancic, Elia Van Wolputte, Hendrik Blockeel:
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints. CoRR abs/1803.11060 (2018) - [i8]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series. CoRR abs/1805.00779 (2018) - [i7]Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt:
DeepProbLog: Neural Probabilistic Logic Programming. CoRR abs/1805.10872 (2018) - [i6]Sebastijan Dumancic, Alberto García-Durán, Mathias Niepert:
On embeddings as an alternative paradigm for relational learning. CoRR abs/1806.11391 (2018) - [i5]Alberto García-Durán, Sebastijan Dumancic, Mathias Niepert:
Learning Sequence Encoders for Temporal Knowledge Graph Completion. CoRR abs/1809.03202 (2018) - 2017
- [j1]Sebastijan Dumancic, Hendrik Blockeel:
An expressive dissimilarity measure for relational clustering using neighbourhood trees. Mach. Learn. 106(9-10): 1523-1545 (2017) - [c4]Sebastijan Dumancic, Hendrik Blockeel:
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation. IJCAI 2017: 1631-1637 - [c3]Toon van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel:
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints. IJCAI 2017: 2871-2877 - [c2]Sebastijan Dumancic, Hendrik Blockeel:
Demystifying Relational Latent Representations. ILP 2017: 63-77 - [i4]Sebastijan Dumancic, Hendrik Blockeel:
Demystifying Relational Latent Representations. CoRR abs/1705.05785 (2017) - 2016
- [c1]Sebastijan Dumancic, Hendrik Blockeel:
An Efficient and Expressive Similarity Measure for Relational Clustering Using Neighbourhood Trees. ECAI 2016: 1674-1675 - [i3]Sebastijan Dumancic, Hendrik Blockeel:
An expressive dissimilarity measure for relational clustering using neighbourhood trees. CoRR abs/1604.08934 (2016) - [i2]Sebastijan Dumancic, Hendrik Blockeel:
Unsupervised Relational Representation Learning via Clustering: Preliminary Results. CoRR abs/1606.08658 (2016) - [i1]Sebastijan Dumancic, Wannes Meert, Hendrik Blockeel:
Theory reconstruction: a representation learning view on predicate invention. CoRR abs/1606.08660 (2016)
Coauthor Index
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