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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 …
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-finite measure …
RER) is investigated under the assumption that the reference measure is a-finite measure …
The generalization error of machine learning algorithms
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 …
of information measures for the generalization error of machine learning algorithms is …
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 …
Asymmetry of the relative entropy in the regularization of empirical risk minimization
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 …
minimization (ERM) with relative entropy regularization (ERM-RER). Two regularizations are …
Empirical risk minimization with relative entropy regularization type-II
F Daunas, I Esnaola, SM Perlaza, HV Poor - 2023 - hal.science
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 …
On the generalization error of meta learning for the Gibbs algorithm
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs
algorithm. Our exact characterization of the expected meta generalization error for the meta …
algorithm. Our exact characterization of the expected meta generalization error for the meta …
Towards optimal inverse temperature in the Gibbs algorithm
This paper explores the problem of selecting optimal hyperparameters in the Gibbs
algorithm to minimize the population risk, specifically focusing on the inverse temperature …
algorithm to minimize the population risk, specifically focusing on the inverse temperature …
Information-theoretic Analysis of Bayesian Test Data Sensitivity
Bayesian inference is often used to quantify uncertainty. Several recent analyses have
rigorously decomposed uncertainty in prediction by Bayesian inference into two types: the …
rigorously decomposed uncertainty in prediction by Bayesian inference into two types: the …
An exact characterization of the generalization error of machine learning algorithms
X Zou, SM Perlaza, I Esnaola, E Altman, HV Poor - 2024 - inria.hal.science
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 …