4 papers
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…
GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models
Qi Qin, Jiajie Zhu, Dali Chen +6
Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and memory costs, hindering lar…
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…
Distributed Reconstruction from Compressive Measurements: Nonconvexity and Heterogeneity
Erbo Li, Qi Qin, Yifan Sun +1
The compressive sensing (CS) and 1-bit CS demonstrate superior efficiency in signal acquisition and resource conservation, while 1-bit CS achieves maximum resource efficiency throu…