Retrieve, Merge, Predict: Augmenting Tables with Data Lakes

N/A

Retrieve, Merge, Predict: Augmenting Tables with Data Lakes Riccardo Cappuzzo, Aimee Coelho, Félix Lefebvre, Paolo Papotti, Gaël Varoquaux To cite this version: Riccardo Cappuzzo, Aimee Coelho, Félix Lefebvre, Paolo Papotti, Gaël Varoquaux. Retrieve, Merge, Predict: Augmenting Tables with Data Lakes. Transactions on Machine Learning Research , 2025. ⟨hal-04509600v2⟩ HAL Id: hal-04509600 https://hal.science/hal-04509600v2 Submitted on 19 May 2025…

saved by

• Rikard Saqe

related reading

• [2011.10427] Dataset Discovery in Data Lakesarxiv.org
• [2603.07146] Fine-Grained Table Retrieval Through the Lens of Complex Queriesarxiv.org
• [2403.02327] Model Lakesarxiv.org
• Metadata-driven Table Union Search: Leveraging Semantics for Restricted Access Data Integrationarxiv.org
• Analysis of Open Government Datasets From a Data Design and Integration Perspectiveopenproceedings.org
• Accurate predictions on small data with a tabular foundation modelnature.com
• Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performancenature.com
• [2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasksarxiv.org
• DataRater: Meta-Learned Dataset Curationarxiv.org
• [2605.15220] Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Timearxiv.org
• Self-Adapting Language Modelsarxiv.org
• [2605.12715] Scaling Laws for Mixture Pretraining Under Data Constraintsarxiv.org