3 papers
stat.ML2026
Multi-environment Invariance Learning with Missing Data
Yiran Jia, Jelena Bradic
Learning models that can handle distribution shifts is a key challenge in domain generalization. Invariance learning, an approach that focuses on identifying features invariant acr…
cs.LG2025
A Connection Between Score Matching and Local Intrinsic Dimension
Eric Yeats, Aaron Jacobson, Darryl Hannan +4
The local intrinsic dimension (LID) of data is a fundamental quantity in signal processing and learning theory, but quantifying the LID of high-dimensional, complex data has been a…
cs.CR2025
Chinese Remainder Theorem Approach to Montgomery-Type Algorithms
Guangwu Xu, Yiran Jia, Yanze Yang
This paper explores the ability of the Chinese Remainder Theorem formalism to model Montgomery-type algorithms. A derivation of CRT based on Qin's Identity gives Montgomery reducti…