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2020 – today
- 2024
[c2]Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux:
CARTE: Pretraining and Transfer for Tabular Learning. ICML 2024- [i6]Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux:
CARTE: pretraining and transfer for tabular learning. CoRR abs/2402.16785 (2024) - [i5]David Holzmüller, Léo Grinsztajn, Ingo Steinwart:
Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data. CoRR abs/2407.04491 (2024) - 2023
- [i4]Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim, Gaël Varoquaux:
Vectorizing string entries for data processing on tables: when are larger language models better? CoRR abs/2312.09634 (2023) - 2022
- [c1]Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux:
Why do tree-based models still outperform deep learning on typical tabular data? NeurIPS 2022 - [i3]Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux:
Why do tree-based models still outperform deep learning on tabular data? CoRR abs/2207.08815 (2022) - [i2]Sid Black, Lee Sharkey, Léo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, Connor Leahy:
Interpreting Neural Networks through the Polytope Lens. CoRR abs/2211.12312 (2022) - 2020
- [i1]Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Léo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines:
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. CoRR abs/2002.12253 (2020)
Coauthor Index
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