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10/28/2022
Beyond calibration: estimating the grouping loss of modern neural networks
Good decision making requires machine-learning models to provide trustwo...
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02/17/2022
Benchmarking missing-values approaches for predictive models on health databases
BACKGROUND: As databases grow larger, it becomes harder to fully control...
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06/01/2021
What's a good imputation to predict with missing values?
How to learn a good predictor on data with missing values? Most efforts ...
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07/03/2020
Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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02/03/2020
Linear predictor on linearly-generated data with missing values: non consistency and solutions
We consider building predictors when the data have missing values. We st...
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02/16/2018
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Learning sparse linear models with two-way interactions is desirable in ...
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06/01/2017
