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Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020

Abstract : 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. This document summarizes a simple survey about experimental procedures, sent to authors of published papers at two leading conferences, NeurIPS 2019 and ICLR 2020. It gives a simple picture of how hyper-parameters are set, how many baselines and datasets are included, or how seeds are used.
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https://hal.archives-ouvertes.fr/hal-02447823
Contributor : Gaël Varoquaux <>
Submitted on : Tuesday, January 21, 2020 - 8:54:00 PM
Last modification on : Wednesday, October 14, 2020 - 4:03:54 AM
Long-term archiving on: : Wednesday, April 22, 2020 - 7:58:21 PM

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  • HAL Id : hal-02447823, version 1

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Xavier Bouthillier, Gaël Varoquaux. Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020. [Research Report] Inria Saclay Ile de France. 2020. ⟨hal-02447823⟩

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