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cs.LG2026
End-to-End Compression for Tabular Foundation Models
Guri Zabërgja, Rafiq Kamel, Arlind Kadra +2
The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundation models. In-context learning m…
cs.LG2023
On the Adversarial Robustness of Graph Contrastive Learning Methods
Filippo Guerranti, Zinuo Yi, Anna Starovoit +3
Contrastive learning (CL) has emerged as a powerful framework for learning representations of images and text in a self-supervised manner while enhancing model robustness against a…