2 citations · 3 across the 13 of their papers we have counts for
13 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…
FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture
Zijian Wang, Pengfei Li, Guangyu Yang +1
Routing-prediction federated learning has emerged as a new paradigm that reframes inter-client heterogeneity as a resource for system-level intelligence: at inference time, the ser…
On Revisiting Entropy for Identifying Mislabeled Images
Chunlei Li, Zixuan Zheng, Yilei Shi +5
Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge…
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 semiparametric two-sample homogeneity test with nonignorable nonresponse using callback data
Xinyu Wang, Tao Yu, Chunlin Wang +1
Testing the homogeneity of two distributions is fundamental in statistics, but classical procedures may fail under nonignorable nonresponse. In many surveys, callback data record r…
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…
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…