3 papers
cs.LG2026
Exploring Fine-Tuning for Tabular Foundation Models
Aditya Tanna, Pratinav Seth, Mohamed Bouadi +1
Tabular Foundation Models (TFMs) have recently shown strong in-context learning capabilities on structured data, achieving zero-shot performance comparable to traditional machine l…
cs.LG2025
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
Aditya Tanna, Pratinav Seth, Mohamed Bouadi +2
Tabular foundation models represent a growing paradigm in structured data learning, extending the benefits of large-scale pretraining to tabular domains. However, their adoption re…
cs.AI2025
Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning
Mohamed Bouadi, Pratinav Seth, Aditya Tanna +1
Tabular data remain the predominant format for real-world applications. Yet, developing effective neural models for tabular data remains challenging due to heterogeneous feature ty…