Arkapal Panda

2 accepted papers

2025

Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label Interdependencies

AISTATS 2025poster

A key challenge in calibrating Multi-Label Classification(MLC) problems is to consider the interdependencies among labels. To address this, in this research we propose an unbiased, differentiable, trainable calibration error estimator for MLC problems by using Copula. Unlike other methods for calibr…

Cited by 0 Source ↗Scholar ↗

2025

Label Dependency Aware Loss for Reliable Multi-Label Medical Image Classification

Aditya Shankar Pal, Arkapal Panda, Utpal Garain

ICASSP 2025accepted

A key challenge in multi-label classification is to model the dependencies between the labels while ensuring proper calibration, as the assumption of label independence often results in inferior classification performance and poor calibration. However, most of the earlier works that modeled label de…

Cited by 0 Source ↗Scholar ↗