1 citations · 1 across the 4 of their papers we have counts for
5 papers
Transfer Learning under Group-Label Shift: A Semiparametric Exponential Tilting Approach
Manli Cheng, Subha Maity, Qinglong Tian +1
We propose a new framework for binary classification in transfer learning settings where both covariate and label distributions may shift between source and target domains. Unlike…
Semiparametric Learning from Open-Set Label Shift Data
Siyan Liu, Yukun Liu, Qinglong Tian +2
We study the open-set label shift problem, where the test data may include a novel class absent from training. This setting is challenging because both the class proportions and th…
Model positive and unlabeled data with a generalized additive density ratio model
Peijun Sang, Yifan Sun, Qinglong Tian +2
We address learning from positive and unlabeled (PU) data, a common setting in which only some positives are labeled and the rest are mixed with negatives. Classical exponential ti…
TabPFN: One Model to Rule Them All?
Qiong Zhang, Yan Shuo Tan, Qinglong Tian +1
Hollmann et al. (Nature 637 (2025) 319-326) recently introduced TabPFN, a transformer-based deep learning model for regression and classification on tabular data, which they claim…
Likelihood-based Nonparametric Receiver Operating Characteristic Curve Analysis in the Presence of Imperfect Reference Standard
Yifan Sun, Peijun Sang, Qinglong Tian +1
In diagnostic studies, researchers frequently encounter imperfect reference standards with some misclassified labels. Treating these as gold standards can bias receiver operating c…