3 papers
stat.ML2026
PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models
Qi Qin, Erbo Li, Ting Wei +4
In high-dimensional tabular learning, feature screening provides a lightweight, model-agnostic way to remove irrelevant features before model fitting. However, scoring raw values d…
stat.ML2025
Label-shift robust federated feature screening for high-dimensional classification
Qi Qin, Erbo Li, Xingxiang Li +3
Distributed and federated learning are important tools for high-dimensional classification of large datasets. To reduce computational costs and overcome the curse of dimensionality…
cs.LG2022
Clustered Federated Learning based on Nonconvex Pairwise Fusion
Xue Yu, Ziyi Liu, Wu Wang +1
This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster opti…