benchmarking 1distribution shift 1foundation models 1model robustness 1out-of-distribution 1tabular data 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.PF2026
Performance Reporting of Mathematical Library Installations with LAAB - An Overview
Aravind Sankaran, Paolo Bientinesi
We present the Linear Algebra Aware Benchmarks (LAAB) framework for systematically assessing and reporting the performance of mathematical library installations on HPC systems. Mat…
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…