Massive datasets and high-capacity models have driven many recent advancements in
computer vision and natural language understanding. This work presents a platform to …

The growing range of applications of Machine Learning (ML) in a multitude of settings
motivates the ability of computing small explanations for predictions made. Small …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

One of the most important paradigm shifts in the use of SAT solvers for solving industrial
problems has been the introduction of clause learning. Clause learning entails adding a …

One of the most prominent application areas for quantum computers is solving hard
constraint satisfaction and optimization problems. However, detailed analyses of the …

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 …

The importance of explanations (XP's) of machine learning (ML) model predictions and of
adversarial examples (AE's) cannot be overstated, with both arguably being essential for the …

Recent work proposed a toolkit PySAT aiming at fast and easy prototyping with propositional
satisfiability (SAT) oracles in Python, which enabled one to exploit the power of the original …

In this work, we aim to leverage prior symbolic knowledge to improve the performance of
deep models. We propose a graph embedding network that projects propositional formulae …

Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers.
Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for …