5 papers · 1 filter
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
Yuanjie Shi, Peihong Li, Zijian Zhang +2
Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extractors, resources typically unav…
Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
Xuesong Jia, Yuanjie Shi, Ziquan Liu +2
Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid…
Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification
Zijian Zhang, Xinyu Chen, Yuanjie Shi +3
Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential fo…
Direct Prediction Set Minimization via Bilevel Conformal Classifier Training
Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia +3
Conformal prediction (CP) is a promising uncertainty quantification framework which works as a wrapper around a black-box classifier to construct prediction sets (i.e., subset of c…
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja +2
Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or…