3 papers
cs.LG2026
Multi-Distribution Robust Conformal Prediction
Yuqi Yang, Ying Jin
In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mi…
cs.CV2026
Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
Yinghao Jin, Xi Yang
Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. Howe…
stat.ME2025
Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage
Ying Jin, Zhimei Ren
Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice,…