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Foundation models, pretrained and readily usable for many downstream tasks, have changed the way we process text, images, and sound. Can we achieve similar breakthroughs for tables? Here I explain why with “CARTE”, we’ve made significant headway.
We just released skrub 0.2.0. This release markedly simplifies learning on complex dataframes.
The highlight of the release is the tabular_learner function, which facilitates creating pipelines that readily perform machine learning on dataframes, adding preprocessing to a scikit-learn compatible learner …
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François Chollet rightfully said that people often underestimate the impact of scikit-learn. I give here a few illustrations to back his claim.
A few days ago, François Chollet (the creator of Keras, the library that that democratized deep learning) posted:
Indeed, scikit-learn continues to be the most popular machine …
A retrospective on last year (2022): I embarked on a new scientific adventure, assembling a team focused on developing machine learning for health and social science. The team has existed for almost a year, and the vision is nice shaping up. Let me share with you illustrations of where we …
Broad decoding models that can specialize to discriminate closely-related mental process with limited data
TL;DR
Decoding models can help isolating which mental processes are implied by the activation of given brain structures. But to support a broad conclusion, they must be trained on many studies, a difficult problem given …
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Join us to work on reinventing data-science practices and tools to produce robust analysis with less data curation.
It is well known that data cleaning and preparation are a heavy burden to the data scientist.
In the dirty data project, we have been conducting machine-learning research …
The year 2020 has undoubtedly been interesting: the covid19 pandemic stroke while I was on a work sabbatical in Montréal, at the MNI and the MILA, and it pushed further my interest in machine learning for health-care. My highlights this year revolve around basic and applied data-science for health.
Highlights …
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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 …
