11.1k citations · 16.3k across the 88 of their papers we have counts for
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Beyond calibration: estimating the grouping loss of modern neural networks
Alexandre Perez-Lebel, Marine Le Morvan, Gaël Varoquaux
The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.…
Reweighting the RCT for generalization: finite sample error and variable selection
Bénédicte Colnet, Julie Josse, Gaël Varoquaux +1
Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain charact…
Why do tree-based models still outperform deep learning on tabular data?
Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux
While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear. We contribute extensive benchmarks of standard and nov…
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek
State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words. To address this issue, we follow the princi…
Benchmarking missing-values approaches for predictive models on health databases
Alexandre Perez-Lebel, Gaël Varoquaux, Marine Le Morvan +2
BACKGROUND: As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values: incomplete observations. These large databa…