TL;DR: I describe a method for hyperparameter optimization by gradient descent.

Most machine …

Announce: first public release of lightning!, a library for large-scale linear classification, regression and ranking in Python. The library was started a couple of years ago by Mathieu Blondel who also contributed the vast majority of source code. I joined recently its development and decided it was about time for …

Together with other scikit-learn developers we've created an umbrella organization for scikit-learn-related projects named scikit-learn-contrib. The idea is for this organization to host projects that are deemed too specific or too experimental to be included in the scikit-learn codebase but still offer an API which is compatible with scikit-learn and …

Recently I've implemented, together with Arnaud Rachez, the SAGA[¹] algorithm in the lightning machine learning library (which by the way, has been recently moved to the new scikit-learn-contrib project). The lightning library uses the same API as scikit-learn but is particularly adapted to online learning. As for the SAGA …

My latests work (with Francis Bach and Alexandre Gramfort) is on the consistency of ordinal regression methods. It has the wildly imaginative …

Cross-validation iterators in scikit-learn are simply generator objects, that is, Python objects that implement the __iter__ method and that for each call to this method return (or more precisely, yield) the indices or a boolean mask for the train and test set. Hence, implementing new cross-validation iterators that behave as …

TL; DR These are some notes on calibration of surrogate loss functions in the context of machine learning. But mostly it is …

In this post I compar several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization …

TL;DR: I've implemented a logistic ordinal regression or proportional odds model. Here is the Python code

The logistic ordinal regression model …

My latest contribution for scikit-learn is an implementation of the isotonic regression model that I coded with Nelle Varoquaux and Alexandre Gramfort …

** Note: this post contains a fair amount of LaTeX, if you don't visualize the math correctly come to its original location **

In …

A little experiment to see what low rank approximation looks like. These are the best rank-k approximations (in the Frobenius norm) to the a natural image for increasing values of k and an original image of rank 512.

Python code can be found here. GIF animation made using ImageMagic's convert …