2 papers
cs.LG2026
QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption
Mattia Sabella, Alberto Archetti, Pietro Pinoli +2
Tabular machine learning systems are frequently trained on data affected by non-uniform corruption, including noisy measurements, missing entries, and feature-specific biases. In p…
cs.CL2025
Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation?
Matteo Spreafico, Ludovica Tassini, Camilla Sancricca +1
Large language models have recently demonstrated their exceptional capabilities in supporting and automating various tasks. Among the tasks worth exploring for testing large langua…