3 papers
cs.LG2025
Efficient Large-Scale Learning of Minimax Risk Classifiers
Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez
Supervised learning with large-scale data usually leads to complex optimization problems, especially for classification tasks with multiple classes. Stochastic subgradient methods…
stat.ML2025
Split Conformal Classification with Unsupervised Calibration
Santiago Mazuelas
Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Ex…
stat.ML2025
On the Optimality of the Median-of-Means Estimator under Adversarial Contamination
Xabier de Juan, Santiago Mazuelas
The Median-of-Means (MoM) is a robust estimator widely used in machine learning that is known to be (minimax) optimal in scenarios where samples are i.i.d. In more grave scenarios,…