Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

Addressing the problem of fairness is crucial to safely use machine learning algorithms to
support decisions with a critical impact on people's lives such as job hiring, child …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Advances in language modeling architectures and the availability of large text corpora have
driven progress in automatic text generation. While this results in models capable of …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

We study the problem of learning a real-valued function that satisfies the Demographic
Parity constraint. It demands the distribution of the predicted output to be independent of the …

Prediction-based decision-making systems are becoming increasingly prevalent in various
domains. Previous studies have demonstrated that such systems are vulnerable to runaway …

We prove a central limit theorem for the entropic transportation cost between subgaussian
probability measures, centered at the population cost. This is the first result which allows for …

With the growing privacy concerns in recommender systems, recommendation unlearning is
getting increasing attention. Existing studies predominantly use training data, ie, model …

Among the myriad of technical approaches and abstract guidelines proposed to the topic of
AI bias, there has been an urgent call to translate the principle of fairness into the …