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stat.ML2026
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…
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,…