Xuesong Jia — accepted AI-conference papers

2026

Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds

Xuesong Jia, Yuanjie Shi, Ziquan Liu, Yi Xu, Yan Yan

AAAI 2026technical

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 probability. To align the uncertainty measured by CP, conformal training methods minimize the size of the prediction se

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2025

Direct Prediction Set Minimization via Bilevel Conformal Classifier Training

Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia, Xiongzhi Chen, Jana Doppa, Yan Yan

ICML 2025poster

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 candidate classes) with provable guarantees. However, standard calibration methods for CP tend to produce large predicti…

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