benchmarking 1distribution shift 1foundation models 1model robustness 1out-of-distribution 1tabular data 1
From the 1 of 2 linked papers with an AI index.
2 papers
cs.CV2026
Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts
Malena Loza, Felipe Grijalva, Eva Milara +3
Tabular-to-image methods that convert tabular data into visual representations have emerged as a novel paradigm for leveraging the high performance of deep learning models. Despite…
cs.LG2026
Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models
Malena Loza, David Chushig-Muzo, Eva Milara +3
The paper empirically evaluates how nine tabular foundation models perform under various out-of-distribution shifts using real-world datasets, finding systematic performance degrad…