research
∙
09/08/2022
Prediction intervals with controlled length in the heteroscedastic Gaussian regression
We tackle the problem of building a prediction interval in heteroscedast...
share
research
∙
08/31/2022
Active learning algorithm through the lens of rejection arguments
Active learning is a paradigm of machine learning which aims at reducing...
research
∙
09/28/2021
Fairness guarantee in multi-class classification
Algorithmic Fairness is an established area of machine learning, willing...
research
∙
02/24/2021
Set-valued classification – overview via a unified framework
Multi-class classification problem is among the most popular and well-st...
research
∙
06/30/2020
Regression with reject option and application to kNN
We investigate the problem of regression where one is allowed to abstain...
research
∙
06/28/2020
Layer Sparsity in Neural Networks
Sparsity has become popular in machine learning, because it can save com...
research
∙
06/12/2020
Fair Regression with Wasserstein Barycenters
We study the problem of learning a real-valued function that satisfies t...
research
∙
06/12/2019
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
We study the problem of fair binary classification using the notion of E...
research
∙
04/29/2019
Minimax semi-supervised confidence sets for multi-class classification
In this work we study the semi-supervised framework of confidence set cl...
research
∙
03/14/2017
On the benefits of output sparsity for multi-label classification
The multi-label classification framework, where each observation can be ...
research
∙
02/07/2014
On the Prediction Performance of the Lasso
Although the Lasso has been extensively studied, the relationship betwee...
research
∙
04/16/2013