12 papers
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…