Since many machine learning algorithms require a distance metric to capture dis/similarities
between data points, metric learning has received much attention during the past decade …

Many applications of AI involve scoring individuals using a learned function of their
attributes. These predictive risk scores are then used to take decisions based on whether the …

A key element of any machine learning algorithm is the use of a function that measures the
dis/similarity between data points. Given a task, such a function can be optimized with a …

In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean
$\theta $ of a square integrable rv Z, around which accurate nonasymptotic confidence …

Similarity learning is a general problem to elicit useful representations by predicting the
relationship between a pair of patterns. This problem is related to various important …

Tournament procedures, recently introduced in the literature, offer an appealing alternative,
from a theoretical perspective at least, to the principle of Empirical Risk Minimization in …

Many applications of AI, ranging from credit lending to medical diagnosis support through
recidivism prediction, involve scoring people using a learned function of their attributes …

In the domain of Big Data, the company's supply chain has a very high-risk exposure and
this must be observed from a preventive perspective, that is, act before such situations occur …

We propose a novel deep metric learning method. Differently from many works in this area,
we define a novel latent space obtained through an autoencoder. The new space, namely S …

M in its cosmos of tasks, models, and methods is as diverse as ever.
While traditionally, we have distinguished only supervised learning tasks like classification …