In this paper, the worst-case probability measure over the data is introduced as a tool for
characterizing the generalization capabilities of machine learning algorithms. More …

The long memory process is a stochastic process with power-type autocorrelation. Such
processes are found worldwide, and those arising in the environmental sciences often have …

The worst-case data-generating (WCDG) probability measure is introduced as a tool for
characterizing the generalization capabilities of machine learning algorithms. Such a WCDG …

The dependence on training data of the Gibbs algorithm (GA) is analytically characterized.
By adopting the expected empirical risk as the performance metric, the sensitivity of the GA …

Various approaches have been developed to upper bound the generalization error of a
supervised learning algorithm. However, existing bounds are often loose and even vacuous …

In this paper, the method of gaps, a technique for deriving closed-form expressions in terms
of information measures for the generalization error of machine learning algorithms is …

The optimality and sensitivity of the empirical risk minimization problem with relative entropy
regularization (ERM-RER) are investigated for the case in which the reference is a σ-finite …

This report presents the solution to the empirical risk minimization with $ f $-divergence
regularization, under mild conditions on $ f $. Under such conditions, the optimal measure is …

The effect of relative entropy asymmetry is analyzed in the context of empirical risk
minimization (ERM) with relative entropy regularization (ERM-RER). Two regularizations are …

The effect of the relative entropy asymmetry is analyzed in the empirical risk minimization
with relative entropy regularization (ERM-RER) problem. A novel regularization is …