111 citations · 169 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…