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 …

Contemporary machine learning applications often involve classification tasks with many
classes. Despite their extensive use, a precise understanding of the statistical properties and …

For a classification task, we usually select an appropriate classifier via model selection. How
to evaluate whether the chosen classifier is optimal? One can answer this question via …

In the context of supervised learning, meta learning uses features, metadata and other
information to learn about the difficulty, behavior, or composition of the problem. Using this …

Henze-Penrose (HP) divergence has been used in many information theory, statistics and
machine learning contexts, including the estimation of two-class Bayes classification error …

Point processes are finding growing applications in numerous fields, such as neuroscience,
high frequency finance and social media. So classic problems of classification and …

Predictability is an emerging metric that quantifies the highest possible prediction accuracy
for a given time series, being widely utilized in assessing known prediction algorithms and …

Bounding the best achievable error probability for binary classification problems is relevant
to many applications including machine learning, signal processing, and information theory …

In the standard pipeline for machine learning model development, several design decisions
are made largely based on trial and error. Take the classification problem as an example …

We propose a new splitting criterion for a meta-learning approach to multiclass classifier
design that adaptively merges the classes into a tree-structured hierarchy of increasingly …