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cs.LG2025
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…
cs.LG2023★ 1 cited
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization
Ziquan Liu, Yi Xu, Xiangyang Ji +1
Recent years have seen the ever-increasing importance of pre-trained models and their downstream training in deep learning research and applications. At the same time, the defense…
cs.LG2019
Evaluating and Boosting Uncertainty Quantification in Classification
Xiaoyang Huang, Jiancheng Yang, Linguo Li +3
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted me…