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Found 10 papers, 1 papers with code
EERO: Early Exit with Reject Option for Efficient Classification with limited budget
The increasing complexity of advanced machine learning models requires innovative approaches to manage computational resources effectively.
Set-valued classification -- overview via a unified framework
Multi-class classification problem is among the most popular and well-studied statistical frameworks.
Fair regression via plug-in estimator and recalibration with statistical guarantees
We study the problem of learning an optimal regression function subject to a fairness constraint.
Regression with reject option and application to kNN
We provide a semi-supervised estimation procedure of the optimal rule involving two datasets: a first labeled dataset is used to estimate both regression function and conditional variance function while a second unlabeled dataset is exploited to calibrate the desired rejection rate.
Layer Sparsity in Neural Networks
Sparsity has become popular in machine learning, because it can save computational resources, facilitate interpretations, and prevent overfitting.
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
We study the problem of fair binary classification using the notion of Equal Opportunity.
On the benefits of output sparsity for multi-label classification
The modern multi-label problems are typically large-scale in terms of number of observations, features and labels, and the amount of labels can even be comparable with the amount of observations.
On the Prediction Performance of the Lasso
Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood.
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Popular sparse estimation methods based on $\ell_1$-relaxation, such as the Lasso and the Dantzig selector, require the knowledge of the variance of the noise in order to properly tune the regularization parameter.
