
Inductive logic programming at 30: a new introduction
Inductive logic programming (ILP) is a form of machine learning. The goa...
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Feature Interactions in XGBoost
In this paper, we investigate how feature interactions can be identified...
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Knowledge Refactoring for Program Induction
Humans constantly restructure knowledge to use it more efficiently. Our ...
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Learning large logic programs by going beyond entailment
A major challenge in inductive logic programming (ILP) is learning large...
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From Statistical Relational to NeuroSymbolic Artificial Intelligence
Neurosymbolic and statistical relational artificial intelligence both i...
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Turning 30: New Ideas in Inductive Logic Programming
Common criticisms of stateoftheart machine learning include poor gene...
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Learning Relational Representations with Autoencoding Logic Programs
Deep learning methods capable of handling relational data have prolifera...
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Learning Sequence Encoders for Temporal Knowledge Graph Completion
Research on link prediction in knowledge graphs has mainly focused on st...
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On embeddings as an alternative paradigm for relational learning
Many realworld domains can be expressed as graphs and, more generally, ...
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On embeddings as alternative paradigm for relational learning
Many realworld domains can be expressed as graphs and, more generally, ...
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DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language tha...
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COBRASTS: A new approach to SemiSupervised Clustering of Time Series
Clustering is ubiquitous in data analysis, including analysis of time se...
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COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
Constraintbased clustering algorithms exploit background knowledge to c...
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COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Clustering is inherently illposed: there often exist multiple valid clu...
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Demystifying Relational Latent Representations
Latent features learned by deep learning approaches have proven to be a ...
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Theory reconstruction: a representation learning view on predicate invention
With this positional paper we present a representation learning view on ...
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ClusteringBased Relational Unsupervised Representation Learning with an Explicit Distributed Representation
The goal of unsupervised representation learning is to extract a new rep...
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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...
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Sebastijan Dumančić
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