Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020 - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2020

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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Dates and versions

hal-02447823 , version 1 (21-01-2020)

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  • HAL Id : hal-02447823 , version 1

Cite

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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