Toward Machine Learning Optimization of Experimental Design
- DOI
- 10.1080/10619127.2021.1881364
- Published
- 2021-01-02
- Container
- Nuclear Physics News
- Publisher
- Informa UK Limited
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1080/10619127.2021.1881364,
title = {Toward Machine Learning Optimization of Experimental Design},
author = {Atılım Güneş Baydin and Kyle Cranmer and Pablo de Castro Manzano and Christophe Delaere and Denis Derkach and Julien Donini and Tommaso Dorigo and Andrea Giammanco and Jan Kieseler and Lukas Layer and Gilles Louppe and Fedor Ratnikov and Giles C. Strong and Mia Tosi and Andrey Ustyuzhanin and Pietro Vischia and Hevjin Yarar},
year = {2021},
journal = {Nuclear Physics News},
doi = {10.1080/10619127.2021.1881364},
url = {https://doi.org/10.1080/10619127.2021.1881364}
}RIS
TY - JOUR TI - Toward Machine Learning Optimization of Experimental Design AU - Atılım Güneş Baydin AU - Kyle Cranmer AU - Pablo de Castro Manzano AU - Christophe Delaere AU - Denis Derkach AU - Julien Donini AU - Tommaso Dorigo AU - Andrea Giammanco AU - Jan Kieseler AU - Lukas Layer AU - Gilles Louppe AU - Fedor Ratnikov AU - Giles C. Strong AU - Mia Tosi AU - Andrey Ustyuzhanin AU - Pietro Vischia AU - Hevjin Yarar PY - 2021 JO - Nuclear Physics News DO - 10.1080/10619127.2021.1881364 UR - https://doi.org/10.1080/10619127.2021.1881364 ER -
APA
Baydin, A. G., Cranmer, K., Manzano, P. D. C., Delaere, C., Derkach, D., Donini, J., Dorigo, T., Giammanco, A., Kieseler, J., Layer, L., Louppe, G., Ratnikov, F., Strong, G. C., Tosi, M., Ustyuzhanin, A., Vischia, P., & Yarar, H. (2021). Toward Machine Learning Optimization of Experimental Design. Nuclear Physics News. https://doi.org/10.1080/10619127.2021.1881364
Source records
- crossref · retrieved 2026-09-26T07:21:47.411Z