MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. - Achille Thin; Nikita Kotelevskii; Jean-Stanislas Denain; Léo Grinsztajn; Alain Durmus; Maxim Panov; Eric Moulines (CoRR, 2020)

Why do tree-based models still outperform deep learning on typical tabular data? - Léo Grinsztajn; Edouard Oyallon; Gaël Varoquaux (NeurIPS, 2022)

Why do tree-based models still outperform deep learning on tabular data? - Léo Grinsztajn; Edouard Oyallon; Gaël Varoquaux (CoRR, 2022)

Interpreting Neural Networks through the Polytope Lens. - Sid Black; Lee Sharkey; Léo Grinsztajn; Eric Winsor; Dan Braun; Jacob Merizian; Kip Parker; Carlos Ramón Guevara; Beren Millidge; Gabriel Alfour; Connor Leahy (CoRR, 2022)

Vectorizing string entries for data processing on tables: when are larger language models better? - Léo Grinsztajn; Edouard Oyallon; Myung Jun Kim; Gaël Varoquaux (CoRR, 2023)

CARTE: Pretraining and Transfer for Tabular Learning. - Myung Jun Kim; Léo Grinsztajn; Gaël Varoquaux (ICML, 2024)

Better by default: Strong pre-tuned MLPs and boosted trees on tabular data. - David Holzmüller; Léo Grinsztajn; Ingo Steinwart (NeurIPS, 2024)

Reconciling deep learning with tabular data. (Réconcilier l'apprentissage profond avec les données tabulaires). - Léo Grinsztajn (2024)