| Why do tree-based models still outperform deep learning on typical tabular data? L Grinsztajn, E Oyallon, G Varoquaux Advances in neural information processing systems 35, 507-520, 2022 | 940 | 2022 |
| Bayesian workflow for disease transmission modeling in Stan L Grinsztajn, E Semenova, CC Margossian, J Riou Statistics in medicine 40 (27), 6209-6234, 2021 | 54 | 2021 |
| Interpreting neural networks through the polytope lens S Black, L Sharkey, L Grinsztajn, E Winsor, D Braun, J Merizian, K Parker, ... arXiv preprint arXiv:2211.12312, 2022 | 15 | 2022 |
| MetFlow: a new efficient method for bridging the gap between Markov chain Monte Carlo and variational inference A Thin, N Kotelevskii, JS Denain, L Grinsztajn, A Durmus, M Panov, ... arXiv preprint arXiv:2002.12253, 2020 | 15 | 2020 |
| CARTE: pretraining and transfer for tabular learning MJ Kim, L Grinsztajn, G Varoquaux arXiv preprint arXiv:2402.16785, 2024 | 1 | 2024 |
| Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data D Holzmüller, L Grinsztajn, I Steinwart arXiv preprint arXiv:2407.04491, 2024 | | 2024 |
| Vectorizing string entries for data processing on tables: when are larger language models better? L Grinsztajn, E Oyallon, MJ Kim, G Varoquaux arXiv preprint arXiv:2312.09634, 2023 | | 2023 |
| Modeling string entries for tabular data prediction: do we need big large language models? L Grinsztajn, MJ Kim, E Oyallon, G Varoquaux NeurIPS 2023 Second Table Representation Learning Workshop, 0 | | |
| Attributing Mode Collapse in the Fine-Tuning of Large Language Models L O’Mahony, L Grinsztajn, H Schoelkopf, S Biderman | | |
