Benchopt : Reproducible, efficient and collaborative optimization benchmarks Article Swipe
Thomas Moreau
,
Mathurin Massias
,
Alexandre Gramfort
,
Pierre Ablin
,
Pierre‐Antoine Bannier
,
Benjamin Charlier
,
Mathieu Dagréou
,
Tom Dupré la Tour
,
Ghislain Durif
,
Cássio F. Dantas
,
Quentin Klopfenstein
,
Johan Larsson
,
En Lai
,
Tanguy Lefort
,
Benoit Malézieux
,
Badr Moufad
,
Binh Tuan Nguyen
,
Alain Rakotomamonjy
,
Zaccharie Ramzi
,
Joseph Salmon
,
Samuel Vaiter
·
YOU?
·
· 2022
· Open Access
·
YOU?
·
· 2022
· Open Access
·
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: ℓ2-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings.
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- Type
- preprint
- Language
- en
- Landing Page
- https://hal.science/hal-03830604
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307352647
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307352647Canonical identifier for this work in OpenAlex
- Title
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Benchopt : Reproducible, efficient and collaborative optimization benchmarksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-06Full publication date if available
- Authors
-
Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre‐Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoit Malézieux, Badr Moufad, Binh Tuan Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel VaiterList of authors in order
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https://hal.science/hal-03830604Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hal.science/hal-03830604Direct OA link when available
- Concepts
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Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.work | 182 |
| abstract_inverted_index.These | 146 |
| abstract_inverted_index.broad | 126 |
| abstract_inverted_index.devil | 170 |
| abstract_inverted_index.field | 36 |
| abstract_inverted_index.hence | 186 |
| abstract_inverted_index.image | 144 |
| abstract_inverted_index.often | 68 |
| abstract_inverted_index.poses | 37 |
| abstract_inverted_index.rapid | 32 |
| abstract_inverted_index.these | 162 |
| abstract_inverted_index.three | 132 |
| abstract_inverted_index.which | 71 |
| abstract_inverted_index.work. | 62 |
| abstract_inverted_index.wrong | 75 |
| abstract_inverted_index.Lasso, | 139 |
| abstract_inverted_index.across | 100 |
| abstract_inverted_index.actual | 16 |
| abstract_inverted_index.allows | 12 |
| abstract_inverted_index.assess | 14 |
| abstract_inverted_index.foster | 180 |
| abstract_inverted_index.impact | 17 |
| abstract_inverted_index.tasks: | 135 |
| abstract_inverted_index.theory | 27 |
| abstract_inverted_index.between | 26 |
| abstract_inverted_index.confirm | 23 |
| abstract_inverted_index.limited | 50 |
| abstract_inverted_index.machine | 7, 98 |
| abstract_inverted_index.methods | 47 |
| abstract_inverted_index.nuanced | 156 |
| abstract_inverted_index.propose | 85 |
| abstract_inverted_index.publish | 94 |
| abstract_inverted_index.result, | 65 |
| abstract_inverted_index.several | 38 |
| abstract_inverted_index.sharing | 119 |
| abstract_inverted_index.showing | 164 |
| abstract_inverted_index.tedious | 60 |
| abstract_inverted_index.Benchopt | 106, 178 |
| abstract_inverted_index.ResNet18 | 141 |
| abstract_inverted_index.compare, | 49 |
| abstract_inverted_index.details. | 174 |
| abstract_inverted_index.findings | 151 |
| abstract_inverted_index.hardware | 104 |
| abstract_inverted_index.learning | 8, 99, 134 |
| abstract_inverted_index.logistic | 137 |
| abstract_inverted_index.methods, | 20 |
| abstract_inverted_index.partial, | 70 |
| abstract_inverted_index.progress | 81 |
| abstract_inverted_index.research | 9, 191 |
| abstract_inverted_index.running, | 118 |
| abstract_inverted_index.showcase | 129 |
| abstract_inverted_index.standard | 133 |
| abstract_inverted_index.training | 142 |
| abstract_inverted_index.Benchopt, | 86 |
| abstract_inverted_index.Numerical | 0 |
| abstract_inverted_index.agreement | 25 |
| abstract_inverted_index.automate, | 91 |
| abstract_inverted_index.community | 111, 185 |
| abstract_inverted_index.consensus | 53 |
| abstract_inverted_index.extending | 121 |
| abstract_inverted_index.findings. | 192 |
| abstract_inverted_index.framework | 89 |
| abstract_inverted_index.highlight | 148 |
| abstract_inverted_index.improving | 187 |
| abstract_inverted_index.languages | 102 |
| abstract_inverted_index.practical | 150, 167 |
| abstract_inverted_index.practice. | 29 |
| abstract_inverted_index.problems, | 163 |
| abstract_inverted_index.profusion | 45 |
| abstract_inverted_index.providing | 113 |
| abstract_inverted_index.reproduce | 92 |
| abstract_inverted_index.research. | 83 |
| abstract_inverted_index.benchmarks | 96, 130, 147 |
| abstract_inverted_index.confronted | 42 |
| abstract_inverted_index.practices, | 56 |
| abstract_inverted_index.simplifies | 107 |
| abstract_inverted_index.usability, | 127 |
| abstract_inverted_index.validation | 1, 66 |
| abstract_inverted_index.challenges: | 39 |
| abstract_inverted_index.conclusions | 76 |
| abstract_inverted_index.demonstrate | 124 |
| abstract_inverted_index.development | 33 |
| abstract_inverted_index.evaluation, | 168 |
| abstract_inverted_index.programming | 101 |
| abstract_inverted_index.regression, | 138 |
| abstract_inverted_index.researchers | 40 |
| abstract_inverted_index.benchmarking | 108 |
| abstract_inverted_index.experiments. | 122 |
| abstract_inverted_index.optimization | 95 |
| abstract_inverted_index.transparency | 51 |
| abstract_inverted_index.collaborative | 88, 181 |
| abstract_inverted_index.off-the-shelf | 115 |
| abstract_inverted_index.architectures. | 105 |
| abstract_inverted_index.classification. | 145 |
| abstract_inverted_index.reproducibility | 189 |
| abstract_inverted_index.state-of-the-art | 160 |
| abstract_inverted_index.ℓ2-regularized | 136 |
| abstract_inverted_index.re-implementation | 61 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 21 |
| citation_normalized_percentile.value | 0.76722385 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |