4 papers
Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
Xabier de Juan, Santiago Mazuelas, Yilun Zhu +1
Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning from noisily-labeled data has…
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…
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…
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,…