Skip to content

Commit

Permalink
Creating 10.21105.joss.05776.crossref.xml
Browse files Browse the repository at this point in the history
  • Loading branch information
editorialbot committed Sep 28, 2023
1 parent 8e52f6e commit 26cc326
Showing 1 changed file with 369 additions and 0 deletions.
369 changes: 369 additions & 0 deletions joss.05776/10.21105.joss.05776.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,369 @@
<?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>20230928T193704-b7b93635dec6c91a17a6a3c2950078839a3e3195</doi_batch_id>
<timestamp>20230928193704</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>09</month>
<year>2023</year>
</publication_date>
<journal_volume>
<volume>8</volume>
</journal_volume>
<issue>89</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>BlackBIRDS: Black-Box Inference foR Differentiable
Simulators</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Arnau</given_name>
<surname>Quera-Bofarull</surname>
<ORCID>https://orcid.org/0000-0001-5055-9863</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Joel</given_name>
<surname>Dyer</surname>
<ORCID>https://orcid.org/0000-0002-8304-8450</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Anisoara</given_name>
<surname>Calinescu</surname>
<ORCID>https://orcid.org/0000-0003-2082-734X</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>J. Doyne</given_name>
<surname>Farmer</surname>
<ORCID>https://orcid.org/0000-0001-7871-073X</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Michael</given_name>
<surname>Wooldridge</surname>
<ORCID>https://orcid.org/0000-0002-9329-8410</ORCID>
</person_name>
</contributors>
<publication_date>
<month>09</month>
<day>28</day>
<year>2023</year>
</publication_date>
<pages>
<first_page>5776</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.05776</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.8377044</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/5776</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.05776</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.05776</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.05776.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="pytorch">
<article_title>PyTorch: An imperative style,
high-performance deep learning library</article_title>
<author>Paszke</author>
<journal_title>Proceedings of the 33rd international
conference on neural information processing systems</journal_title>
<cYear>2019</cYear>
<unstructured_citation>Paszke, A., Gross, S., Massa, F.,
Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein,
N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison,
M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S.
(2019). PyTorch: An imperative style, high-performance deep learning
library. In Proceedings of the 33rd international conference on neural
information processing systems. Curran Associates
Inc.</unstructured_citation>
</citation>
<citation key="normflows">
<article_title>Normflows: A PyTorch package for normalizing
flows</article_title>
<author>Stimper</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>86</issue>
<volume>8</volume>
<doi>10.21105/joss.05361</doi>
<cYear>2023</cYear>
<unstructured_citation>Stimper, V., Liu, D., Campbell, A.,
Berenz, V., Ryll, L., Schölkopf, B., &amp; Hernández-Lobato, J. M.
(2023). Normflows: A PyTorch package for normalizing flows. Journal of
Open Source Software, 8(86), 5361.
https://doi.org/10.21105/joss.05361</unstructured_citation>
</citation>
<citation key="gradabm">
<article_title>Differentiable agent-based
epidemiology</article_title>
<author>Chopra</author>
<journal_title>Proceedings of the 2023 international
conference on autonomous agents and multiagent systems</journal_title>
<isbn>978-1-4503-9432-1</isbn>
<cYear>2023</cYear>
<unstructured_citation>Chopra, A., Rodríguez, A.,
Subramanian, J., Quera-Bofarull, A., Krishnamurthy, B., Prakash, B. A.,
&amp; Raskar, R. (2023). Differentiable agent-based epidemiology.
Proceedings of the 2023 International Conference on Autonomous Agents
and Multiagent Systems, 1848–1857.
ISBN: 978-1-4503-9432-1</unstructured_citation>
</citation>
<citation key="hep">
<article_title>Differentiable programming in high-energy
physics</article_title>
<author>Baydin</author>
<cYear>2020</cYear>
<unstructured_citation>Baydin, A. G., NYU, K. C., Feickert,
M., Gray, L., Heinrich, L., NYU, A. H., Neubauer, A. M. V. M., Pearkes,
J., Simpson, N., Smith, N., &amp; others. (2020). Differentiable
programming in high-energy physics.</unstructured_citation>
</citation>
<citation key="ii">
<article_title>Indirect inference</article_title>
<author>Gourieroux</author>
<journal_title>Journal of applied
econometrics</journal_title>
<issue>S1</issue>
<volume>8</volume>
<cYear>1993</cYear>
<unstructured_citation>Gourieroux, C., Monfort, A., &amp;
Renault, E. (1993). Indirect inference. Journal of Applied Econometrics,
8(S1), S85–S118.</unstructured_citation>
</citation>
<citation key="smm">
<article_title>Applying the method of simulated moments to
estimate a small agent-based asset pricing model</article_title>
<author>Franke</author>
<journal_title>Journal of Empirical Finance</journal_title>
<issue>5</issue>
<volume>16</volume>
<doi>10.1016/j.jempfin.2009.06.006</doi>
<cYear>2009</cYear>
<unstructured_citation>Franke, R. (2009). Applying the
method of simulated moments to estimate a small agent-based asset
pricing model. Journal of Empirical Finance, 16(5), 804–815.
https://doi.org/10.1016/j.jempfin.2009.06.006</unstructured_citation>
</citation>
<citation key="mala">
<article_title>Exponential convergence of Langevin
distributions and their discrete approximations</article_title>
<author>Roberts</author>
<journal_title>Bernoulli</journal_title>
<doi>10.2307/3318418</doi>
<cYear>1996</cYear>
<unstructured_citation>Roberts, G. O., &amp; Tweedie, R. L.
(1996). Exponential convergence of Langevin distributions and their
discrete approximations. Bernoulli, 341–363.
https://doi.org/10.2307/3318418</unstructured_citation>
</citation>
<citation key="hmc">
<article_title>Hybrid Monte Carlo</article_title>
<author>Duane</author>
<journal_title>Physics letters B</journal_title>
<issue>2</issue>
<volume>195</volume>
<cYear>1987</cYear>
<unstructured_citation>Duane, S., Kennedy, A. D., Pendleton,
B. J., &amp; Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B,
195(2), 216–222.</unstructured_citation>
</citation>
<citation key="bissiri">
<article_title>A general framework for updating belief
distributions</article_title>
<author>Bissiri</author>
<journal_title>Journal of the Royal Statistical Society:
Series B (Statistical Methodology)</journal_title>
<issue>5</issue>
<volume>78</volume>
<doi>10.1111/rssb.12158</doi>
<cYear>2016</cYear>
<unstructured_citation>Bissiri, P. G., Holmes, C., &amp;
Walker, S. G. (2016). A general framework for updating belief
distributions. Journal of the Royal Statistical Society: Series B
(Statistical Methodology), 78(5), 1103.
https://doi.org/10.1111/rssb.12158</unstructured_citation>
</citation>
<citation key="gvi">
<article_title>An optimization-centric view on Bayes’ rule:
Reviewing and generalizing variational inference</article_title>
<author>Knoblauch</author>
<journal_title>Journal of Machine Learning
Research</journal_title>
<issue>132</issue>
<volume>23</volume>
<cYear>2022</cYear>
<unstructured_citation>Knoblauch, J., Jewson, J., &amp;
Damoulas, T. (2022). An optimization-centric view on Bayes’ rule:
Reviewing and generalizing variational inference. Journal of Machine
Learning Research, 23(132), 1–109.</unstructured_citation>
</citation>
<citation key="ai4abm">
<article_title>Bayesian calibration of differentiable
agent-based models</article_title>
<author>Quera-Bofarull</author>
<journal_title>ICLR Workshop on AI for Agent-based
Modelling</journal_title>
<cYear>2023</cYear>
<unstructured_citation>Quera-Bofarull, A., Chopra, A.,
Calinescu, A., Wooldridge, M., &amp; Dyer, J. (2023). Bayesian
calibration of differentiable agent-based models. ICLR Workshop on AI
for Agent-Based Modelling.</unstructured_citation>
</citation>
<citation key="dae">
<article_title>Some challenges of calibrating differentiable
agent-based models</article_title>
<author>Quera-Bofarull</author>
<journal_title>ICML Differentiable Almost Everything
Workshop</journal_title>
<cYear>2023</cYear>
<unstructured_citation>Quera-Bofarull, A., Dyer, J.,
Calinescu, A., &amp; Wooldridge, M. (2023). Some challenges of
calibrating differentiable agent-based models. ICML Differentiable
Almost Everything Workshop.</unstructured_citation>
</citation>
<citation key="sbi">
<article_title>Sbi: A toolkit for simulation-based
inference</article_title>
<author>Tejero-Cantero</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>52</issue>
<volume>5</volume>
<doi>10.21105/joss.02505</doi>
<cYear>2020</cYear>
<unstructured_citation>Tejero-Cantero, A., Boelts, J.,
Deistler, M., Lueckmann, J.-M., Durkan, C., Gonçalves, P. J., Greenberg,
D. S., &amp; Macke, J. H. (2020). Sbi: A toolkit for simulation-based
inference. Journal of Open Source Software, 5(52), 2505.
https://doi.org/10.21105/joss.02505</unstructured_citation>
</citation>
<citation key="blit">
<article_title>Black-it: A ready-to-use and easy-to-extend
calibration kit for agent-based models</article_title>
<author>Benedetti</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>79</issue>
<volume>7</volume>
<doi>10.21105/joss.04622</doi>
<cYear>2022</cYear>
<unstructured_citation>Benedetti, M., Catapano, G., Sclavis,
F. D., Favorito, M., Glielmo, A., Magnanimi, D., &amp; Muci, A. (2022).
Black-it: A ready-to-use and easy-to-extend calibration kit for
agent-based models. Journal of Open Source Software, 7(79), 4622.
https://doi.org/10.21105/joss.04622</unstructured_citation>
</citation>
<citation key="pyvbmc">
<article_title>PyVBMC: Efficient Bayesian inference in
python</article_title>
<author>Huggins</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>86</issue>
<volume>8</volume>
<doi>10.21105/joss.05428</doi>
<cYear>2023</cYear>
<unstructured_citation>Huggins, B., Li, C., Tobaben, M.,
Aarnos, M. J., &amp; Acerbi, L. (2023). PyVBMC: Efficient Bayesian
inference in python. Journal of Open Source Software, 8(86), 5428.
https://doi.org/10.21105/joss.05428</unstructured_citation>
</citation>
<citation key="abcpy">
<article_title>ABCpy: A high-performance computing
perspective to approximate Bayesian computation</article_title>
<author>Dutta</author>
<journal_title>Journal of Statistical
Software</journal_title>
<issue>7</issue>
<volume>100</volume>
<doi>10.18637/jss.v100.i07</doi>
<cYear>2021</cYear>
<unstructured_citation>Dutta, R., Schoengens, M.,
Pacchiardi, L., Ummadisingu, A., Widmer, N., Künzli, P., Onnela, J.-P.,
&amp; Mira, A. (2021). ABCpy: A high-performance computing perspective
to approximate Bayesian computation. Journal of Statistical Software,
100(7), 1–38.
https://doi.org/10.18637/jss.v100.i07</unstructured_citation>
</citation>
<citation key="pyabc">
<article_title>pyABC: Efficient and robust easy-to-use
approximate Bayesian computation</article_title>
<author>Schälte</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>74</issue>
<volume>7</volume>
<doi>10.21105/joss.04304</doi>
<cYear>2022</cYear>
<unstructured_citation>Schälte, Y., Klinger, E., Alamoudi,
E., &amp; Hasenauer, J. (2022). pyABC: Efficient and robust easy-to-use
approximate Bayesian computation. Journal of Open Source Software,
7(74), 4304. https://doi.org/10.21105/joss.04304</unstructured_citation>
</citation>
<citation key="radev2023bayesflow">
<article_title>BayesFlow: Amortized Bayesian Workflows With
Neural Networks</article_title>
<author>Radev</author>
<journal_title>Journal of Open Source
Software</journal_title>
<issue>89</issue>
<volume>8</volume>
<doi>10.21105/joss.05702</doi>
<cYear>2023</cYear>
<unstructured_citation>Radev, S. T., Schmitt, M.,
Schumacher, L., Elsemüller, L., Pratz, V., Schälte, Y., Köthe, U., &amp;
Bürkner, P.-C. (2023). BayesFlow: Amortized Bayesian Workflows With
Neural Networks. Journal of Open Source Software, 8(89), 5702.
https://doi.org/10.21105/joss.05702</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>

0 comments on commit 26cc326

Please sign in to comment.