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Awesome Amortized Inference

+

Awesome +Contributions Welcome +License: CC0

+

Welcome to the Awesome Amortized Inference repository! +This is a curated list of resources, including overviews, software, papers, and other resources related to amortized inference. +Feel free to explore the entries below and use the provided BibTeX information for citation purposes. +This is a community-driven project which is currently maintained by Marvin Schmitt. +Contributions are always welcome, see CONTRIBUTING.md for a contribution guide.

+

This awesome list currently has some overlap with the awesome-neural-sbi list (Link) because +amortized inference has gained populatity in the context of simulation-based inference (SBI) with neural networks. +However, there is a trend towards broader amortized inference methods that are not necessarily simulation-based. +This list aims to cover all amortized inference methods, including but not limited to simulation-based inference. +We highly recommend checking out the awesome-neural-sbi list for more resources on modern simulation-based inference with neural networks.

+
+

🚧 Under construction 🚧 +This repository is a development version under construction. The list of resources is just a small set for debugging and development purposes. +For now, we focus on developing features that we think could be useful for the community (e.g., BibTeX-first structure). +Then, we will evaluate whether this project has the potential to be useful for the community in addition to existing lists +that are specifically designated to simulation-based inference and not amortized inference (which doesn't need to be simulation-based). +Once we figure out the direction and deem the project potentially useful for the community, we'll launch and add an extensive set of resources +-- hopefully with the help of many awesome people from the community 🧡

+
+

Contents

+ +

Overview Articles

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    +
  • +

    Neural Methods for Amortized Inference (2024)
    TLDR: Overview paper of amortized point estimators and full posterior estimators.
    by Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser
    [Paper]

    +
    +Show BibTeX +
    
+@misc{zammit-mangion2024neural,
+title = {Neural Methods for Amortized Inference},
+publisher = {arXiv},
+year = {2024},
+author = {Zammit-Mangion, Andrew and Sainsbury-Dale, Matthew and Huser, Raphaël}
+}
+
+
    +
    • +
  • +

    Normalizing flows for probabilistic modeling and inference (2021)
    Reading this paper? Please consider contributing a TLDR summary.
    by George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
    [Paper]

    +
    +Show BibTeX +
    
+@article{papamakarios2021normalizing,
+title = {Normalizing flows for probabilistic modeling and inference},
+year = {2021},
+publisher = {JMLR.org},
+volume = {22},
+number = {1},
+issn = {1532-4435},
+journal = {J. Mach. Learn. Res.},
+month = {jan},
+articleno = {57},
+numpages = {64},
+author = {Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji}
+}
+
+
    +
    • +
  • +

    The frontier of simulation-based inference (2020)
    Reading this paper? Please consider contributing a TLDR summary.
    by Kyle Cranmer, Johann Brehmer, Gilles Louppe
    [Paper]

    +
    +Show BibTeX +
    
+@article{Cranmer2020,
+title = {The frontier of simulation-based inference},
+volume = {117},
+ISSN = {1091-6490},
+DOI = {10.1073/pnas.1912789117},
+number = {48},
+journal = {Proceedings of the National Academy of Sciences},
+publisher = {Proceedings of the National Academy of Sciences},
+year = {2020},
+pages = {30055-30062},
+author = {Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles}
+}
+
+
    +
    • +
+

Software

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    +
  • +

    BayesFlow: Amortized Bayesian Workflows With Neural Networks
    [Code]

    +
    +Show BibTeX +
    
+@article{radev2023bayesflow,
+doi = {10.21105/joss.05702},
+year = {2023},
+publisher = {The Open Journal},
+volume = {8},
+number = {89},
+pages = {5702},
+title = {BayesFlow: Amortized Bayesian Workflows With Neural Networks},
+journal = {Journal of Open Source Software},
+author = {Radev, Stefan T. and Schmitt, Marvin and Schumacher, Lukas and Elsemüller, Lasse and Pratz, Valentin and Schälte, Yannik and Köthe, Ullrich and Bürkner, Paul-Christian}
+}
+
+
    +
    • +
  • +

    sbi: A toolkit for simulation-based inference
    [Code]

    +
    +Show BibTeX +
    
+@article{tejero-cantero2020sbi,
+doi = {10.21105/joss.02505},
+year = {2020},
+publisher = {The Open Journal},
+volume = {5},
+number = {52},
+pages = {2505},
+title = {sbi: A toolkit for simulation-based inference},
+journal = {Journal of Open Source Software},
+author = {Tejero-Cantero, Alvaro and Boelts, Jan and Deistler, Michael and Lueckmann, Jan-Matthis and Durkan, Conor and Gonçalves, Pedro J. and Greenberg, David S. and Macke, Jakob H.}
+}
+
+
    +
    • +
+

Methodological Papers

+
    +
  • +

    ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems (2024)
    Reading this paper? Please consider contributing a TLDR summary.
    by Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann

    +
    +Show BibTeX +
    
+@misc{orozco2024aspire,
+Title = {ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems},
+Year = {2024},
+Eprint = {arXiv:2405.05398},
+author = {Orozco, Rafael and Siahkoohi, Ali and Louboutin, Mathias and Herrmann, Felix J.}
+}
+
+
    +
    • +
  • +

    Sensitivity-Aware Amortized Bayesian Inference (2024)
    TLDR: Efficient amortized sensitivity analyses with context variables
    by Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Koethe, Stefan T. Radev
    [Code] [Paper]

    +
    +Show BibTeX +
    
+@article{elsemueller2024sensitivity,
+title = {Sensitivity-Aware Amortized Bayesian Inference},
+journal = {Transactions on Machine Learning Research},
+issn = {2835-8856},
+year = {2024},
+author = {Elsem{\"u}ller, Lasse and Olischl{\"a}ger, Hans and Schmitt, Marvin and B{\"u}rkner, Paul-Christian and Koethe, Ullrich and Radev, Stefan T.}
+}
+
+
    +
    • +
  • +

    Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference (2023)
    Reading this paper? Please consider contributing a TLDR summary.
    by Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
    [Paper]

    +
    +Show BibTeX +
    
+@article{dax2023neural,
+title = {Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference},
+volume = {130},
+ISSN = {1079-7114},
+DOI = {10.1103/physrevlett.130.171403},
+number = {17},
+journal = {Physical Review Letters},
+publisher = {American Physical Society (APS)},
+year = {2023},
+author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P\"{u}rrer, Michael and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch\"{o}lkopf, Bernhard}
+}
+
+
    +
    • +
  • +

    JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models (2023)
    Reading this paper? Please consider contributing a TLDR summary.
    by Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner
    [Paper]

    +
    +Show BibTeX +
    
+@inproceedings{radev2023jana,
+title = {{JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models}},
+booktitle = {Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence},
+pages = {1695--1706},
+year = {2023},
+volume = {216},
+series = {Proceedings of Machine Learning Research},
+publisher = {PMLR},
+author = {Radev, Stefan T. and Schmitt, Marvin and Pratz, Valentin and Picchini, Umberto and K\"othe, Ullrich and B\"urkner, Paul-Christian}
+}
+
+
    +
    • +
+

Application Papers

+
    +
  • Evaluating Sparse Galaxy Simulations via Out-of-Distribution Detection and Amortized Bayesian Model Comparison (2024)
    Reading this paper? Please consider contributing a TLDR summary.
    by Lingyi Zhou, Stefan T. Radev, William H. Oliver, Aura Obreja, Zehao Jin, Tobias Buck
    [Paper]
    +Show BibTeX +
    
+@inproceedings{zhou2024EvaluatingSparseGalaxy,
+title = {Evaluating {{Sparse Galaxy Simulations}} via {{Out-of-Distribution Detection}} and {{Amortized Bayesian Model Comparison}}},
+booktitle = {38th {{Conference}} on {{Neural Information Processing Systems}}},
+year = {2024},
+author = {Zhou, Lingyi and Radev, Stefan T. and Oliver, William H. and Obreja, Aura and Jin, Zehao and Buck, Tobias}
+}
+
+
    +
    • +
+

Uncategorized

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    +
  • Flow Matching for Scalable Simulation-Based Inference (2023)
    by Jonas Bernhard Wildberger, Maximilian Dax, Simon Buchholz, Stephen R Green, Jakob H. Macke, Bernhard Schölkopf
    [Paper] [Code]
    +Show BibTeX +
    
+@inproceedings{wildberger2023flow,
+title = {Flow Matching for Scalable Simulation-Based Inference},
+booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
+year = {2023},
+url = {https://openreview.net/forum?id=D2cS6SoYlP},
+author = {Wildberger, Jonas Bernhard and Dax, Maximilian and Buchholz, Simon and Green, Stephen R and Macke, Jakob H. and Sch{\"o}lkopf, Bernhard}
+}
+
+
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    • +
+ +
+
+ + + diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..19c53fb --- /dev/null +++ b/robots.txt @@ -0,0 +1,4 @@ +User-agent: * +Disallow: +Allow: / +Sitemap: https://awesome-amortized-inference.bayesflow.org/sitemap.xml diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..62fc7e1 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,6 @@ + + + + https://awesome-amortized-inference.bayesflow.org/ + +