Machine learning systems are becoming increasingly ubiquitous. These systems's adoption
has been expanding, accelerating the shift towards a more algorithmic society, meaning that …

Many researchers motivate explainable AI with studies showing that human-AI team
performance on decision-making tasks improves when the AI explains its recommendations …

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread
adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the …

A trade-off between accuracy and fairness is almost taken as a given in the existing literature
on fairness in machine learning. Yet, it is not preordained that accuracy should decrease …

In this work we identify continual learning (CL) methods' inherent differences in sequential
decision attribution. In the sequential learning process inconsistent decision attribution may …

We prove that $\Omega (s\log p) $ samples suffice to learn a sparse Gaussian directed
acyclic graph (DAG) from data, where $ s $ is the maximum Markov blanket size. This …

Labeling a real network dataset is specially expensive in computer security, as an expert
has to ponder several factors before assigning each label. This paper describes an …

Instilling trust in high-stakes applications of machine learning is becoming essential. Trust
may be decomposed into four dimensions: basic accuracy, reliability, human interaction, and …

There has been recent interest in improving performance of simple models for multiple
reasons such as interpretability, robust learning from small data, deployment in memory …

Transitions from stable to unstable states occurring in dynamical systems can be sudden
leading to catastrophic failure and huge revenue loss. For detecting these transitions during …