3 papers
cs.LG2026
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…
cs.LG2026
Distribution-informed Efficient Conformal Prediction for Full Ranking
Wenbo Liao, Huipeng Huang, Chen Jia +3
Quantifying uncertainty is critical for the safe deployment of ranking models in real-world applications. Recent work offers a rigorous solution using conformal prediction in a ful…
cs.AI2025
Multi-Condition Conformal Selection
Qingyang Hao, Wenbo Liao, Bingyi Jing +1
Selecting high-quality candidates from large-scale datasets is critically important in resource-constrained applications such as drug discovery, precision medicine, and the alignme…