2 citations · 3 across the 11 of their papers we have counts for
11 papers
Semiparametric Receiver Operating Characteristic Analysis in the Presence of an Imperfect Reference Standard via a Box-Cox Density Ratio Model
Yi Chang, Siyan Liu, Qinglong Tian +1
Receiver operating characteristic (ROC) analysis is commonly used to evaluate the diagnostic accuracy of continuous biomarkers. In practice, the true disease status may be unavaila…
In-Context Positive-Unlabeled Learning
Siyan Liu, Yi Chang, Manli Cheng +2
Positive-unlabeled (PU) learning addresses binary classification when only a set of labeled positives is available alongside a pool of unlabeled samples drawn from a mixture of pos…
A goodness-of-fit test for the logistic propensity score model under nonignorable missing data
Manli Cheng, Yangjianchen Xu, Qinglong Tian +1
Logistic regression is widely used to model the propensity score in the analysis of nonignorable missing data. However, goodness-of-fit testing for this propensity score model has…
Neyman-Pearson multiclass classification under label noise via empirical likelihood
Qiong Zhang, Qinglong Tian, Pengfei Li
In many classification problems, misclassification costs are highly asymmetric, while training labels are often corrupted due to measurement error, annotator variability, or advers…
Semiparametric Joint Inference for Sensitivity and Specificity at the Youden-Optimal Cut-Off
Siyan Liu, Qinglong Tian, Chunlin Wang +1
Sensitivity and specificity evaluated at an optimal diagnostic cut-off are fundamental measures of classification accuracy when continuous biomarkers are used for disease diagnosis…
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…
Modeling positive and unlabeled data with a generalized additive density ratio model
Peijun Sang, Yifan Sun, Qinglong Tian +2
This paper focuses on learning from positive and unlabeled (PU) data, where only some positives are labeled and the rest are mixed with negatives. Classical exponential tilting mod…
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…
Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
Siyan Liu, Chi-Kuang Yeh, Xin Zhang +2
This study introduces a new approach to addressing positive and unlabeled (PU) data through the double exponential tilting model (DETM). Traditional methods often fall short becaus…