2 papers
cs.IR2026
Explaining the "Why": A Unified Framework for the Additive Attribution of Changes in Arbitrary Measures
Changsheng Zhou, Dajun Chen, Zhitao Shen +3
Explaining why aggregated measures change is a critical challenge in data analytics that existing systems struggle to address. While current attribution methods exist, they lack a…
stat.ML2025
Perturbations in the Orthogonal Complement Subspace for Efficient Out-of-Distribution Detection
Zhexiao Huang, Weihao He, Shutao Deng +4
Out-of-distribution (OOD) detection is essential for deploying deep learning models in open-world environments. Existing approaches, such as energy-based scoring and gradient-proje…