111 citations · 169 across the 15 of their papers we have counts for
19 papers
Learning logic programs by discovering higher-order abstractions
Céline Hocquette, Sebastijan Dumančić, Andrew Cropper
We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold. We impleme…
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction
Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
As machine learning models, specifically neural networks, are becoming increasingly popular, there are concerns regarding their trustworthiness, specially in safety-critical applic…
SaDe: Learning Models that Provably Satisfy Domain Constraints
Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
In many real world applications of machine learning, models have to meet certain domain-based requirements that can be expressed as constraints (e.g., safety-critical constraints i…
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans +1
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP t…
Inductive logic programming at 30: a new introduction
Andrew Cropper, Sebastijan Dumančić
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns…
Feature Interactions in XGBoost
Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results…
Knowledge Refactoring for Inductive Program Synthesis
Sebastijan Dumancic, Tias Guns, Andrew Cropper
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introd…
Neural Probabilistic Logic Programming in DeepProbLog
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learni…
Learning Relational Representations with Auto-encoding Logic Programs
Sebastijan Dumancic, Tias Guns, Wannes Meert +1
Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order lo…
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entit…
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally…
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
Toon Van Craenendonck, Wannes Meert, Sebastijan Dumancic +1
Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset…
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning tech…
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
Toon Van Craenendonck, Sebastijan Dumančić, Elia Van Wolputte +1
Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is…
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Toon Van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel
Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowi…
Demystifying Relational Latent Representations
Sebastijan Dumančić, Hendrik Blockeel
Latent features learned by deep learning approaches have proven to be a powerful tool for machine learning. They serve as a data abstraction that makes learning easier by capturing…
Theory reconstruction: a representation learning view on predicate invention
Sebastijan Dumancic, Wannes Meert, Hendrik Blockeel
With this positional paper we present a representation learning view on predicate invention. The intention of this proposal is to bridge the relational and deep learning communitie…
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation
Sebastijan Dumancic, Hendrik Blockeel
The goal of unsupervised representation learning is to extract a new representation of data, such that solving many different tasks becomes easier. Existing methods typically focus…
An expressive dissimilarity measure for relational clustering using neighbourhood trees
Sebastijan Dumancic, Hendrik Blockeel
Clustering is an underspecified task: there are no universal criteria for what makes a good clustering. This is especially true for relational data, where similarity can be based o…