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cs.LG2026
Using Set Learning to Improve Set-Valued Classifiers on Long-tailed Data
Kevin Kasa, Scott C. Lowe, Graham W. Taylor
Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets per input rather than fixed-si…
cs.LG2024
Adapting Prediction Sets to Distribution Shifts Without Labels
Kevin Kasa, Zhiyu Zhang, Heng Yang +1
Recently there has been a surge of interest to deploy confidence set predictions rather than point predictions in machine learning. Unfortunately, the effectiveness of such predict…
cs.LG2023★ 1 cited
Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data
Kevin Kasa, Graham W. Taylor
Conformal prediction has emerged as a rigorous means of providing deep learning models with reliable uncertainty estimates and safety guarantees. Yet, its performance is known to d…