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08/16/2023
Learning Logic Programs by Discovering Higher-Order Abstractions
Discovering novel abstractions is important for human-level AI. We intro...
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03/02/2023
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction
As machine learning models, specifically neural networks, are becoming i...
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01/08/2023
A Divide-Align-Conquer Strategy for Program Synthesis
A major bottleneck in search-based program synthesis is the exponentiall...
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12/01/2021
SaDe: Learning Models that Provably Satisfy Domain Constraints
With increasing real world applications of machine learning, models are ...
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08/25/2021
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey
Neural-symbolic and statistical relational artificial intelligence both ...
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02/21/2021
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learn...
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08/18/2020
Inductive logic programming at 30: a new introduction
Inductive logic programming (ILP) is a form of machine learning. The goa...
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07/11/2020
Feature Interactions in XGBoost
In this paper, we investigate how feature interactions can be identified...
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04/21/2020
Knowledge Refactoring for Program Induction
Humans constantly restructure knowledge to use it more efficiently. Our ...
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04/21/2020
Learning large logic programs by going beyond entailment
A major challenge in inductive logic programming (ILP) is learning large...
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03/18/2020
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Neuro-symbolic and statistical relational artificial intelligence both i...
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02/25/2020
Turning 30: New Ideas in Inductive Logic Programming
Common criticisms of state-of-the-art machine learning include poor gene...
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03/29/2019
Learning Relational Representations with Auto-encoding Logic Programs
Deep learning methods capable of handling relational data have prolifera...
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09/10/2018
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Research on link prediction in knowledge graphs has mainly focused on st...
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06/29/2018
On embeddings as an alternative paradigm for relational learning
Many real-world domains can be expressed as graphs and, more generally, ...
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06/29/2018
On embeddings as alternative paradigm for relational learning
Many real-world domains can be expressed as graphs and, more generally, ...
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05/28/2018
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language tha...
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05/02/2018
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series
Clustering is ubiquitous in data analysis, including analysis of time se...
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03/29/2018
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints
Constraint-based clustering algorithms exploit background knowledge to c...
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01/30/2018
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Clustering is inherently ill-posed: there often exist multiple valid clu...
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05/16/2017
Demystifying Relational Latent Representations
Latent features learned by deep learning approaches have proven to be a ...
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06/28/2016
Theory reconstruction: a representation learning view on predicate invention
With this positional paper we present a representation learning view on ...
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06/28/2016
Clustering-Based 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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04/29/2016