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Pieter Gijsbers

2 accepted papers

2024

Croissant: A Metadata Format for ML-Ready Datasets

NeurIPS 2024spotlight

Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, por…

2021

OpenML Benchmarking Suites

NeurIPS 2021poster

Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and reporting of benchmarks. We enable this through software tools th…

Cited by 175SourceScholar