Timezone: »
AI as statistical methods for imperfect theories
Gael Varoquaux
Event URL: https://openreview.net/forum?id=rzWxx4jAH79 »
Science has progressed by reasoning on what models could not predict because they were missing important ingredients. And yet without correct models, standard statistical methods for scientific evidence are not sound. Here I argue that machine-learning methodology provides solutions to ground reasoning about empirically evidence more on models’ predictions, and less on their ingredients.
Author Information
Gael Varoquaux (INRIA)
More from the Same Authors
-
2021 Spotlight: What’s a good imputation to predict with missing values? »
Marine Le Morvan · Julie Josse · Erwan Scornet · Gael Varoquaux -
2023 Workshop: Table Representation Learning Workshop »
Madelon Hulsebos · Bojan Karlaš · Haoyu Dong · Gael Varoquaux · Laurel Orr · Pengcheng Yin -
2022 Poster: Why do tree-based models still outperform deep learning on typical tabular data? »
Leo Grinsztajn · Edouard Oyallon · Gael Varoquaux -
2021 Poster: What’s a good imputation to predict with missing values? »
Marine Le Morvan · Julie Josse · Erwan Scornet · Gael Varoquaux -
2020 Poster: NeuMiss networks: differentiable programming for supervised learning with missing values. »
Marine Le Morvan · Julie Josse · Thomas Moreau · Erwan Scornet · Gael Varoquaux -
2020 Oral: NeuMiss networks: differentiable programming for supervised learning with missing values. »
Marine Le Morvan · Julie Josse · Thomas Moreau · Erwan Scornet · Gael Varoquaux -
2019 Poster: Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing »
Meyer Scetbon · Gael Varoquaux -
2019 Spotlight: Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing »
Meyer Scetbon · Gael Varoquaux -
2019 Poster: Manifold-regression to predict from MEG/EEG brain signals without source modeling »
David Sabbagh · Pierre Ablin · Gael Varoquaux · Alexandre Gramfort · Denis A. Engemann -
2017 : Scikit-learn & nilearn: Democratisation of machine learning for brain imaging (INRIA) »
Gael Varoquaux -
2017 : Invited Talk: "Tales from fMRI: Learning from limited labeled data" »
Gael Varoquaux -
2017 Poster: Learning Neural Representations of Human Cognition across Many fMRI Studies »
Arthur Mensch · Julien Mairal · Danilo Bzdok · Bertrand Thirion · Gael Varoquaux -
2016 Poster: Learning brain regions via large-scale online structured sparse dictionary learning »
Elvis DOHMATOB · Arthur Mensch · Gael Varoquaux · Bertrand Thirion -
2015 Poster: Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data »
Danilo Bzdok · Michael Eickenberg · Olivier Grisel · Bertrand Thirion · Gael Varoquaux -
2013 Poster: Mapping paradigm ontologies to and from the brain »
Yannick Schwartz · Bertrand Thirion · Gael Varoquaux