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Olivier Grisel

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3 papers

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NeurIPS Conference 2023 Conference Paper

False Discovery Proportion control for aggregated Knockoffs

Alexandre Blain
Bertrand Thirion
Olivier Grisel
Pierre Neuvial

Controlled variable selection is an important analytical step in various scientific fields, such as brain imaging or genomics. In these high-dimensional data settings, considering too many variables leads to poor models and high costs, hence the need for statistical guarantees on false positives. Knockoffs are a popular statistical tool for conditional variable selection in high dimension. However, they control for the expected proportion of false discoveries (FDR) and not the actual proportion of false discoveries (FDP). We present a new method, KOPI, that controls the proportion of false discoveries for Knockoff-based inference. The proposed method also relies on a new type of aggregation to address the undesirable randomness associated with classical Knockoff inference. We demonstrate FDP control and substantial power gains over existing Knockoff-based methods in various simulation settings and achieve good sensitivity/specificity tradeoffs on brain imaging data.

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NeurIPS Conference 2015 Conference Paper

Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data

Danilo Bzdok
Michael Eickenberg
Olivier Grisel
Bertrand Thirion
Gael Varoquaux

Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets. Existing neuroimaging methods typically perform either discovery of unknown neural structure or testing of neural structure associated with mental tasks. However, testing hypotheses on the neural correlates underlying larger sets of mental tasks necessitates adequate representations for the observations. We therefore propose to blend representation modelling and task classification into a unified statistical learning problem. A multinomial logistic regression is introduced that is constrained by factored coefficients and coupled with an autoencoder. We show that this approach yields more accurate and interpretable neural models of psychological tasks in a reference dataset, as well as better generalization to other datasets.

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JMLR Journal 2011 Journal Article

Scikit-learn: Machine Learning in Python

Fabian Pedregosa
Gaël Varoquaux
Alexandre Gramfort
Vincent Michel
Bertrand Thirion
Olivier Grisel
Mathieu Blondel
Peter Prettenhofer

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2011. ( edit, beta )

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