Optimization inequalities cheatsheet
Most proofs in optimization consist in using inequalities for a particular function class in some creative way. This is a cheatsheet with inequalities that I use most often. It considers …
Most proofs in optimization consist in using inequalities for a particular function class in some creative way. This is a cheatsheet with inequalities that I use most often. It considers …
My friend Rémi Leblond has recently uploaded to ArXiv our preprint on an asynchronous version of the SAGA optimization algorithm.
The main contribution is to develop a parallel (fully asynchronous, no locks) variant of the SAGA algorighm. This is a stochastic variance-reduced method for general optimization, specially adapted for problems …
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 …
Due to lack of time and interest, I'm no longer maintaining this project. Feel free to grab the sources from https://github.com/fabianp/nbgallery and fork the project.
TL;DR I created a gallery for IPython/Jupyter notebooks. Check it out :-)
A couple of months ago I put online …
Last Friday was PyData Paris, in words of the organizers, ''a gathering of users and developers of data analysis tools in Python''.
The organizers did a great job in putting together and the event started already with a full room for Gael's keynote
My take-away message from the talks is …