The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

Explainability in the field of event detection is a new emerging research area. For
practitioners and users alike, explainability is essential to ensuring that models are widely …

Biomedical data exhibit high-dimensional complexity in its internal structure and are
susceptible to noise interference, making classification tasks in biomedical data highly …

We present FOLD-SE, an efficient, explainable machine learning algorithm for classification
tasks given tabular data containing numerical and categorical values. The (explainable) …

We introduce ABALearn, an automated algorithm that learns Assumption-Based
Argumentation (ABA) frameworks from training data consisting of positive and negative …

We argue that various extensions proposed for Prolog—tabling, constraints, parallelism,
coroutining, etc.—must be integrated seamlessly in a single system. We also discuss how …

Mainstream machine learning methods lack interpretability, explainability, incrementality,
and data-economy. We propose using logic programming to rectify these problems. We …

FOLD-RM is an explainable machine learning classification algorithm that uses training data
to create a set of classification rules. In this paper, we introduce CON-FOLD which extends …

Machine learning models that automate decision-making are increasingly being used in
consequential areas such as loan approvals, pretrial bail, hiring, and many more …

Machine learning models that automate decision-making are increasingly being used in
consequential areas such as loan approvals, pretrial bail approval, hiring, and many more …