17 papers
Robust Conformalized Selection with Noisy Responses
Chengyao Yu, Hongxin Wei, Bingyi Jing
Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such as reliable labeling, drug disc…
Improving Backward Conformal Prediction via Non-Conformity Score Transformation
Junxian Liu, Hao Zeng, Hongxin Wei
Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees. While CP yields uncontrolled pre…
A Regret Perspective on Online Multiple Testing
Qingyang Hao, Kongchang Zhou, Fang Kong +1
Online Multiple Testing (OMT), a fundamental pillar of sequential statistical inference, traditionally evaluates the False Discovery Rate (FDR) and statistical power in isolation,…
Semi-Supervised Conformal Prediction With Unlabeled Nonconformity Score
Xuanning Zhou, Zihao Shi, Hao Zeng +3
Conformal prediction (CP) is a powerful framework for uncertainty quantification, generating prediction sets with coverage guarantees. Split conformal prediction relies on labeled…
Model-agnostic Selective Labeling with Provable Statistical Guarantees
Huipeng Huang, Wenbo Liao, Huajun Xi +3
Obtaining high-quality labels for large datasets is expensive, requiring massive annotations from human experts. While AI models offer a cost-effective alternative by predicting la…
Provable Model Provenance Set for Large Language Models
Xiaoqi Qiu, Hao Zeng, Zhiyu Hou +1
The growing prevalence of unauthorized model usage and misattribution has increased the need for reliable model provenance analysis. However, existing methods largely rely on heuri…