Code is important in applied research. Here are some of the code I have written over the years.
- peerannot: a package to make transparent and reproducible comparisons between annotation algorithms, especially in the context of citizen science.
- OrganizationFiles: This repository provides some tools, advice and guidelines for researchers working in applied mathematics, statistics or machine learning.
- BenchOpt: a package to make transparent and reproducible comparisons between optimization algorithms
- PlantNet-300K: a subset of the Pl@ntNet database, with about 300k labeled images (plant species) and 1k classes. The dataset is available on Zenodo.
- Celer: a fast Lasso solver (associated ICML2018 paper “Dual Extrapolation for Faster Lasso Solvers”), pdf, slides
- sparse-ho: a fast hyper-parameter package to select the best Lasso parameter efficiently (associated to ICML2020 paper “Implicit differentiation of Lasso-type models for hyperparameter optimization”, pdf)
- matlab toolboxes for statistics and image processing (this is legacy), I don’t use Matlab anymore.
More on my Github Page