2 papers
cs.LG2026
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin +1
Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for…
cs.LG2026
Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning
Matheus Camilo da Silva, Gabriel Gustavo Costanzo, Andrea de Lorenzo +1
Many machine learning classification tasks involve imbalanced datasets, which are often subject to over-sampling techniques aimed at improving model performance. However, these tec…