Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Creating pull request for 10.21105.joss.06839 #6221

Closed
wants to merge 4 commits into from
Closed
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
354 changes: 354 additions & 0 deletions joss.06839/10.21105.joss.06839.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,354 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20241205182029-27a8c34a19a6fcdcd00350534da01ca75941dea6</doi_batch_id>
<timestamp>20241205182029</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>12</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>104</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>AutoRA: Automated Research Assistant for Closed-Loop
Empirical Research</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Sebastian</given_name>
<surname>Musslick</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
<institution><institution_name>Osnabrück University, Germany</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0000-0002-8896-639X</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Benjamin</given_name>
<surname>Andrew</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
</affiliations>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Chad C.</given_name>
<surname>Williams</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0000-0003-2016-5614</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Joshua T. S.</given_name>
<surname>Hewson</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
</affiliations>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Sida</given_name>
<surname>Li</surname>
<affiliations>
<institution><institution_name>University of Chicago, USA</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0009-0009-7849-1923</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Ioana</given_name>
<surname>Marinescu</surname>
<affiliations>
<institution><institution_name>Princeton University, USA</institution_name></institution>
</affiliations>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Marina</given_name>
<surname>Dubova</surname>
<affiliations>
<institution><institution_name>University of Indiana, USA</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0000-0001-5264-0489</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>George T.</given_name>
<surname>Dang</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0009-0008-1566-8245</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Younes</given_name>
<surname>Strittmatter</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
<institution><institution_name>Princeton University, USA</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0000-0002-3414-2838</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>John G.</given_name>
<surname>Holland</surname>
<affiliations>
<institution><institution_name>Brown University, USA</institution_name></institution>
</affiliations>
<ORCID>https://orcid.org/0000-0001-6845-8657</ORCID>
</person_name>
</contributors>
<publication_date>
<month>12</month>
<day>05</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6839</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06839</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.14278950</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6839</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06839</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06839</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06839.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="hewson_bayesian_2023">
<article_title>Bayesian machine scientist for model
discovery in psychology</article_title>
<author>Hewson</author>
<journal_title>NeurIPS 2023 AI for science
workshop</journal_title>
<cYear>2023</cYear>
<unstructured_citation>Hewson, J., Strittmatter, Y.,
Marinescu, I., Williams, C., &amp; Musslick, S. (2023). Bayesian machine
scientist for model discovery in psychology. NeurIPS 2023 AI for Science
Workshop.
https://openreview.net/forum?id=XHFfvzlQ1n</unstructured_citation>
</citation>
<citation key="musslick_evaluation_2023">
<article_title>An evaluation of experimental sampling
strategies for autonomous empirical research in cognitive
science</article_title>
<author>Musslick</author>
<volume>45</volume>
<cYear>2023</cYear>
<unstructured_citation>Musslick, S., Hewson, J. T., Andrew,
B. W., Strittmatter, Y., Williams, C. C., Dang, G. T., Dubova, M., &amp;
Holland, J. G. (2023). An evaluation of experimental sampling strategies
for autonomous empirical research in cognitive science. 45,
1386–1392.</unstructured_citation>
</citation>
<citation key="dubova_against_2022">
<article_title>Against theory-motivated experimentation in
science</article_title>
<author>Dubova</author>
<journal_title>MetaArXiv</journal_title>
<volume>24</volume>
<doi>10.31222/osf.io/ysv2u</doi>
<cYear>2022</cYear>
<unstructured_citation>Dubova, M., Moskvichev, A., &amp;
Zollman, K. (2022). Against theory-motivated experimentation in science.
MetaArXiv, 24.
https://doi.org/10.31222/osf.io/ysv2u</unstructured_citation>
</citation>
<citation key="palan_prolific_2018">
<article_title>Prolific. Ac—A subject pool for online
experiments</article_title>
<author>Palan</author>
<journal_title>Journal of Behavioral and Experimental
Finance</journal_title>
<volume>17</volume>
<doi>10.1016/j.jbef.2017.12.004</doi>
<issn>2214-6350</issn>
<cYear>2018</cYear>
<unstructured_citation>Palan, S., &amp; Schitter, C. (2018).
Prolific. Ac—A subject pool for online experiments. Journal of
Behavioral and Experimental Finance, 17, 22–27.
https://doi.org/10.1016/j.jbef.2017.12.004</unstructured_citation>
</citation>
<citation key="pedregosa2011scikit">
<article_title>Scikit-learn: Machine learning in
Python</article_title>
<author>Pedregosa</author>
<journal_title>Journal of Machine Learning
Research</journal_title>
<volume>12</volume>
<cYear>2011</cYear>
<unstructured_citation>Pedregosa, F., Varoquaux, G.,
Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
Prettenhofer, P., Weiss, R., Dubourg, V., &amp; others. (2011).
Scikit-learn: Machine learning in Python. Journal of Machine Learning
Research, 12, 2825–2830.</unstructured_citation>
</citation>
<citation key="petersen2021deep">
<article_title>Deep symbolic regression: Recovering
mathematical expressions from data via risk-seeking policy
gradients</article_title>
<author>Petersen</author>
<journal_title>International conference on learning
representations</journal_title>
<doi>10.48550/arXiv.1912.04871</doi>
<cYear>2021</cYear>
<unstructured_citation>Petersen, B. K., Larma, M. L.,
Mundhenk, T. N., Santiago, C. P., Kim, S. K., &amp; Kim, J. T. (2021).
Deep symbolic regression: Recovering mathematical expressions from data
via risk-seeking policy gradients. International Conference on Learning
Representations.
https://doi.org/10.48550/arXiv.1912.04871</unstructured_citation>
</citation>
<citation key="binz2024centaur">
<article_title>Centaur: A foundation model of human
cognition</article_title>
<author>Binz</author>
<journal_title>arXiv preprint
arXiv:2410.20268</journal_title>
<doi>10.48550/arXiv.2410.20268</doi>
<cYear>2024</cYear>
<unstructured_citation>Binz, M., Akata, E., Bethge, M.,
Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C.,
Eckstein, M. K., Éltető, N., &amp; others. (2024). Centaur: A foundation
model of human cognition. arXiv Preprint arXiv:2410.20268.
https://doi.org/10.48550/arXiv.2410.20268</unstructured_citation>
</citation>
<citation key="weinhardt2024computational">
<article_title>Computational discovery of human
reinforcement learning dynamics from choice behavior</article_title>
<author>Weinhardt</author>
<journal_title>NeurIPS 2024 workshop on behavioral machine
learning</journal_title>
<cYear>2024</cYear>
<unstructured_citation>Weinhardt, D., Eckstein, M. K., &amp;
Musslick, S. (2024). Computational discovery of human reinforcement
learning dynamics from choice behavior. NeurIPS 2024 Workshop on
Behavioral Machine Learning.
https://openreview.net/forum?id=x2WDZrpgmB</unstructured_citation>
</citation>
<citation key="landajuela_unified_2022">
<article_title>A unified framework for deep symbolic
regression</article_title>
<author>Landajuela</author>
<journal_title>Advances in neural information processing
systems</journal_title>
<volume>35</volume>
<cYear>2022</cYear>
<unstructured_citation>Landajuela, M., Lee, C. S., Yang, J.,
Glatt, R., Santiago, C. P., Aravena, I., Mundhenk, T., Mulcahy, G.,
&amp; Petersen, B. K. (2022). A unified framework for deep symbolic
regression. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho,
&amp; A. Oh (Eds.), Advances in neural information processing systems
(Vol. 35, pp. 33985–33998). Curran Associates, Inc.
https://proceedings.neurips.cc/paper_files/paper/2022/file/dbca58f35bddc6e4003b2dd80e42f838-Paper-Conference.pdf</unstructured_citation>
</citation>
<citation key="musslick2024perspective">
<article_title>Automating the practice of
science–opportunities, challenges, and implications</article_title>
<author>Musslick</author>
<journal_title>Proceedings of the National Academy of
Sciences</journal_title>
<unstructured_citation>Musslick, S., Bartlett, L. K.,
Chandramouli, S. H., Dubova, M., Gobet, F., Griffiths, T. L., Hullman,
J., King, R. D., Kutz, J. N., Lucas, C. G., Mahesh, S., Pestilli, F.,
Sloman, S. J., &amp; Holmes, W. R. (in press). Automating the practice
of science–opportunities, challenges, and implications. Proceedings of
the National Academy of Sciences.</unstructured_citation>
</citation>
<citation key="cranmer_discovering_2020">
<article_title>Discovering symbolic models from deep
learning with inductive biases</article_title>
<author>Cranmer</author>
<journal_title>Advances in Neural Information Processing
Systems</journal_title>
<volume>33</volume>
<doi>10.48550/arXiv.2006.11287</doi>
<cYear>2020</cYear>
<unstructured_citation>Cranmer, M., Sanchez Gonzalez, A.,
Battaglia, P., Xu, R., Cranmer, K., Spergel, D., &amp; Ho, S. (2020).
Discovering symbolic models from deep learning with inductive biases.
Advances in Neural Information Processing Systems, 33, 17429–17442.
https://doi.org/10.48550/arXiv.2006.11287</unstructured_citation>
</citation>
<citation key="guimera_bayesian_2020">
<article_title>A Bayesian machine scientist to aid in the
solution of challenging scientific problems</article_title>
<author>Guimerà</author>
<journal_title>Science advances</journal_title>
<issue>5</issue>
<volume>6</volume>
<doi>10.1126/sciadv.aav6971</doi>
<issn>2375-2548</issn>
<cYear>2020</cYear>
<unstructured_citation>Guimerà, R., Reichardt, I.,
Aguilar-Mogas, A., Massucci, F. A., Miranda, M., Pallarès, J., &amp;
Sales-Pardo, M. (2020). A Bayesian machine scientist to aid in the
solution of challenging scientific problems. Science Advances, 6(5),
eaav6971. https://doi.org/10.1126/sciadv.aav6971</unstructured_citation>
</citation>
<citation key="musslick2024">
<article_title>Closed-loop scientific discovery in the
behavioral sciences</article_title>
<author>Musslick</author>
<doi>10.31234/osf.io/c2ytb</doi>
<cYear>2024</cYear>
<unstructured_citation>Musslick, S., Strittmatter, Y., &amp;
Dubova, M. (2024). Closed-loop scientific discovery in the behavioral
sciences. https://doi.org/10.31234/osf.io/c2ytb</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.06839/10.21105.joss.06839.pdf
Binary file not shown.
Loading