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cs.LG2026
Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration
Lening Zhao, Qipeng Zhan, Li Shen
Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining. An ideal calibrator should correct nonlinear miscalibration, scale…
cs.LG2026
Generative Cross-Entropy: A Strictly Proper Loss for Data-Efficient Classification
Qipeng Zhan, Zhuoping Zhou, Li Shen
Cross-entropy (CE) is the default training loss for supervised classification, but its sample efficiency is limited when labels are scarce. Existing remedies primarily act on the d…
cs.LG2026
Bi-Lipschitz Autoencoder With Injectivity Guarantee
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +2
Autoencoders are widely used for dimensionality reduction, based on the assumption that high-dimensional data lies on low-dimensional manifolds. Regularized autoencoders aim to pre…