The growing literature on" benign overfitting" in overparameterized models has been mostly
restricted to regression or binary classification settings; however, most success stories of …

We propose an extensive analysis of the behavior of majority votes in binary classification. In
particular, we introduce a risk bound for majority votes, called the C-bound, that takes into …

How can we select the best performing data-driven model? How can we rigorously estimate
its generalization error? Statistical Learning Theory (SLT) answers these questions by …

How can we select the best performing data‐driven model? How can we rigorously estimate
its generalization error? Statistical learning theory (SLT) answers these questions by …

The support vector machine (SVM) is a well-established classification method whose name
refers to the particular training examples, called support vectors, that determine the …

The speed with which a learning algorithm converges as it is presented with more data is a
central problem in machine learning—a fast rate of convergence means less data is needed …

Capturing predictor-dependent correlations amongst the elements of a multivariate
response vector is fundamental to numerous applied domains, including neuroscience …

The support vector machine (SVM) and minimum Euclidean norm least squares regression
are two fundamentally different approaches to fitting linear models, but they have recently …

We address the problem of randomized learning and generalization of fair and private
classifiers. From one side we want to ensure that sensitive information does not unfairly …

In this paper we bound the risk of the Gibbs and Bayes classifiers (GC and BC), when the
prior is defined in terms of the data generating distribution, and the posterior is defined in …