Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks model
The privacy-protected algorithm (PPA) is pivotal in the realm of machine learning, especially
for handling sensitive data types, such as medical and financial records. PPA enables two …
for handling sensitive data types, such as medical and financial records. PPA enables two …
Generalization analysis of machine learning algorithms via the worst-case data-generating probability measure
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 …
characterizing the generalization capabilities of machine learning algorithms. More …
On the validation of Gibbs algorithms: Training datasets, test datasets and their aggregation
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 …
By adopting the expected empirical risk as the performance metric, the sensitivity of the GA …
Analysis of the relative entropy asymmetry in the regularization of empirical risk minimization
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 …
with relative entropy regularization (ERM-RER) problem. A novel regularization is …
The worst-case data-generating probability measure
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 …
characterizing the generalization capabilities of machine learning algorithms. More …
The worst-case data-generating probability measure in statistical learning
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 …
characterizing the generalization capabilities of machine learning algorithms. Such a WCDG …
Empirical risk minimization with f-divergence regularization in statistical learning
JFD Torres, I Esnaola, SM Perlaza, HV Poor - 2023 - hal.science
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 …
regularization, under mild conditions on $ f $. Under such conditions, the optimal measure is …
Empirical risk minimization with relative entropy regularization
The empirical risk minimization (ERM) problem with relative entropy regularization (ERM-
RER) is investigated under the assumption that the reference measure is a σ-finite measure …
RER) is investigated under the assumption that the reference measure is a σ-finite measure …
Empirical risk minimization with relative entropy regularization type-II
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 …
with relative entropy regularization (ERM-RER) problem. A novel regularization is …
Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
The solution to empirical risk minimization with $ f $-divergence regularization (ERM-$ f $
DR) is presented under mild conditions on $ f $. Under such conditions, the optimal measure …
DR) is presented under mild conditions on $ f $. Under such conditions, the optimal measure …