404 citations · 821 across the 23 of their papers we have counts for
25 papers
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection
Jonas Soenen, Elia Van Wolputte, Vincent Vercruyssen +2
Most anomaly detection systems try to model normal behavior and assume anomalies deviate from it in diverse manners. However, there may be patterns in the anomalies as well. Ideall…
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…
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
Kshitij Goyal, Wannes Meert, Hendrik Blockeel +4
Internet based businesses and products (e.g. e-commerce, music streaming) are becoming more and more sophisticated every day with a lot of focus on improving customer satisfaction.…
Combining Predictions under Uncertainty: The Case of Random Decision Trees
Florian Busch, Moritz Kulessa, Eneldo Loza Mencía +1
A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class. The latter takes un…
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…