Sebastijan Dumančić

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Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning. The goa...
08/18/2020 ∙ by Andrew Cropper, et al. ∙ 8 ∙ share

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Feature Interactions in XGBoost

In this paper, we investigate how feature interactions can be identified...
07/11/2020 ∙ by Kshitij Goyal, et al. ∙ 32 ∙ share

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Knowledge Refactoring for Program Induction

Humans constantly restructure knowledge to use it more efficiently. Our ...
04/21/2020 ∙ by Sebastijan Dumančić, et al. ∙ 8 ∙ share

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Learning large logic programs by going beyond entailment

A major challenge in inductive logic programming (ILP) is learning large...
04/21/2020 ∙ by Andrew Cropper, et al. ∙ 4 ∙ share

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From Statistical Relational to Neuro-Symbolic Artificial Intelligence

Neuro-symbolic and statistical relational artificial intelligence both i...
03/18/2020 ∙ by Luc De Raedt, et al. ∙ 15 ∙ share

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Turning 30: New Ideas in Inductive Logic Programming

Common criticisms of state-of-the-art machine learning include poor gene...
02/25/2020 ∙ by Andrew Cropper, et al. ∙ 12 ∙ share

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Learning Relational Representations with Auto-encoding Logic Programs

Deep learning methods capable of handling relational data have prolifera...
03/29/2019 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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Learning Sequence Encoders for Temporal Knowledge Graph Completion

Research on link prediction in knowledge graphs has mainly focused on st...
09/10/2018 ∙ by Alberto García-Durán, et al. ∙ 0 ∙ share

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On embeddings as an alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...
06/29/2018 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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On embeddings as alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...
06/29/2018 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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DeepProbLog: Neural Probabilistic Logic Programming

We introduce DeepProbLog, a probabilistic logic programming language tha...
05/28/2018 ∙ by Robin Manhaeve, et al. ∙ 0 ∙ share

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COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series

Clustering is ubiquitous in data analysis, including analysis of time se...
05/02/2018 ∙ by Toon Van Craenendonck, et al. ∙ 0 ∙ share

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COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints

Constraint-based clustering algorithms exploit background knowledge to c...
03/29/2018 ∙ by Toon Van Craenendonck, et al. ∙ 0 ∙ share

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COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints

Clustering is inherently ill-posed: there often exist multiple valid clu...
01/30/2018 ∙ by Toon Van Craenendonck, et al. ∙ 0 ∙ share

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Demystifying Relational Latent Representations

Latent features learned by deep learning approaches have proven to be a ...
05/16/2017 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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Theory reconstruction: a representation learning view on predicate invention

With this positional paper we present a representation learning view on ...
06/28/2016 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation

The goal of unsupervised representation learning is to extract a new rep...
06/28/2016 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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An expressive dissimilarity measure for relational clustering using neighbourhood trees

Clustering is an underspecified task: there are no universal criteria fo...
04/29/2016 ∙ by Sebastijan Dumančić, et al. ∙ 0 ∙ share

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Sebastijan Dumančić

Featured Co-authors

Mathias Niepert

Thomas Demeester

Giuseppe Marra

Luc De Raedt

Hendrik Blockeel

Tias Guns

Alberto García-Durán

Andrew Cropper

Angelika Kimmig

Wannes Meert

Stephen H. Muggleton

Inductive logic programming at 30: a new introduction

Feature Interactions in XGBoost

Knowledge Refactoring for Program Induction

Learning large logic programs by going beyond entailment

From Statistical Relational to Neuro-Symbolic Artificial Intelligence

Turning 30: New Ideas in Inductive Logic Programming

Learning Relational Representations with Auto-encoding Logic Programs

Learning Sequence Encoders for Temporal Knowledge Graph Completion

On embeddings as an alternative paradigm for relational learning

On embeddings as alternative paradigm for relational learning

DeepProbLog: Neural Probabilistic Logic Programming

COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series

COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints

COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints

Demystifying Relational Latent Representations

Theory reconstruction: a representation learning view on predicate invention

Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation

An expressive dissimilarity measure for relational clustering using neighbourhood trees