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A simple survey asking authors of two leading machine-learning conferences a few quantitative questions on their experimental procedures.
How do machine-learning researchers run their empirical validation? In the context of a push for improved reproducibility and benchmarking, this question is important to develop new tools for model comparison. We …
My current research spans wide: from brain sciences to core data science. My overall interest is to build methodology drawing insights from data for questions that have often been addressed qualitatively. If I can highlight a few publications from 2019 [1], the common thread would be computational statistics, from dirty …
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Given two set of observations, are they drawn from the same distribution? Our paper Comparing distributions: l1 geometry improves kernel two-sample testing at the NeurIPS 2019 conference revisits this classic statistical problem known as “two-sample testing”.
This post explains the context and the paper with a bit of hand …
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An important acknowledgement for a different view of doing science: open, collaborative, and more than a proof of concept.
A few days ago, Loïc Estève, Alexandre Gramfort, Olivier Grisel, Bertrand Thirion, and myself received the “Académie des Sciences Inria prize for transfer”, for our contributions to the scikit-learn project …
From a scientific perspective, 2018 [1] was once again extremely exciting thank to awesome collaborators (at Inria, with DirtyData, and our local scikit-learn team). Rather than going over everything that we did in 2018, I would like to give a few highlights: We published major work using machine learning to …
We have just announced that a foundation will be supporting scikit-learn at Inria [1]: scikit-learn.fondation-inria.fr
This is an exciting turn for us, because it enables us to receive private funding. As a result, we will be able to have secure employment for some existing core …
Two weeks ago, we held a scikit-learn sprint in Austin and Paris. Here is a brief report, on progresses and challenges.
Several sprints
We actually held two sprint in Austin: one open sprint, at the scipy conference sprints, which was open to new contributors, and one core sprint, for more …
In my opinion the scientific highlights of 2017 for my team were on multivariate predictive analysis for brain imaging: a brain decoder more efficient and faster than alternatives, improvement clinical predictions by predicting jointly multiple traits of subjects, decoding based on the raw time-series of brain activity, and a personnal …
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Scientific progress calls for reproducing results. Due to limited resources, this is difficult even in computational sciences. Yet, reproducibility is only a means to an end. It is not enough by itself to enable new scientific results. Rather, new discoveries must build on reuse and modification of the state …
Two week ago, we held in Paris a large international sprint on scikit-learn. It was incredibly productive and fun, as always. We are still busy merging in the work, but I think that know is a good time to try to summarize the sprint.
We had a …
