Si avvisa che 

in data 21-09-2023, alle ore 14:30 precise

presso il Politecnico di Milano, in aula 16B.2.1, 

nell’ambito delle attività del MOX, si svolgerà il seguente seminario: 

Sesia Matteo, University of Southern California 

Titolo:

Adaptive conformal classification with noisy labels

Abstract:

This work develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, enabling more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches. This is made possible by a precise theoretical characterization of the effective coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through new calibration algorithms. Our solution is flexible and can leverage different modeling assumptions about the label contamination process, while requiring no knowledge about the data distribution or the inner workings of the machine-learning classifier. The advantages of the proposed methods are demonstrated through extensive simulations and an application to object classification with the CIFAR-10H image data set. 

Reference: http://export.arxiv.org/abs/2309.05092

Il link per seguire il seminario online sarà reso disponibile pochi minuti prima dell’avvio del seminario al seguente

Link: https://mox.polimi.it/mox-seminars/?id_evento=2315

Tutti gli interessati sono cordialmente invitati a partecipare,

Laura Sangalli