Search Results for author:
Found 9 papers, 4 papers with code
Beyond calibration: estimating the grouping loss of modern neural networks
Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities.
Benchmarking missing-values approaches for predictive models on health databases
Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning.
What’s a good imputation to predict with missing values?
In fact, we show that on perfectly imputed data the best regression function will generally be discontinuous, which makes it hard to learn.
What's a good imputation to predict with missing values?
In fact, we show that on perfectly imputed data the best regression function will generally be discontinuous, which makes it hard to learn.
NeuMiss networks: differentiable programming for supervised learning with missing values.
We provide an upper bound on the Bayes risk of NeuMiss networks, and show that they have good predictive accuracy with both a number of parameters and a computational complexity independent of the number of missing data patterns.
NeuMiss networks: differentiable programming for supervised learning with missing values
We provide an upper bound on the Bayes risk of NeuMiss networks, and show that they have good predictive accuracy with both a number of parameters and a computational complexity independent of the number of missing data patterns.
Linear predictor on linearly-generated data with missing values: non consistency and solutions
In the particular Gaussian case, it can be written as a linear function of multiway interactions between the observed data and the various missing-value indicators.
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Learning sparse linear models with two-way interactions is desirable in many application domains such as genomics.
Supervised Quantile Normalisation
Quantile normalisation is a popular normalisation method for data subject to unwanted variations such as images, speech, or genomic data.
