diff --git a/resources.bib b/resources.bib index 501a7ac..701a26e 100644 --- a/resources.bib +++ b/resources.bib @@ -4,7 +4,8 @@ @article{altekruger2023conditional year = {2023}, journal = {Transactions on Machine Learning Research}, issn = {2835-8856}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {diagnostics; theory} } @inproceedings{arruda2024anamortized, @@ -15,7 +16,8 @@ @inproceedings{arruda2024anamortized awesome-category = {method}, awesome-tldr = {Neural posterior estimation for hierarchical models, where the NPE is used in a first stage on a local level and then repeatedly used for global inference leveraging amortization.}, awesome-link-paper = {https://openreview.net/forum?id=uCdcXRuHnC}, - awesome-link-code = {https://github.com/arrjon/Amortized-NLME-Models/tree/ICML2024} + awesome-link-code = {https://github.com/arrjon/Amortized-NLME-Models/tree/ICML2024}, + awesome-tags = {parameter estimation; hierarchical models} } @misc{bahl2024advancing, @@ -27,7 +29,8 @@ @misc{bahl2024advancing primaryclass = {hep-ph}, publisher = {arXiv}, archiveprefix = {arXiv}, - awesome-category = {application} + awesome-category = {application}, + awesome-tags = {physics; simulation-based} } @article{bieringer2021measuring, @@ -38,7 +41,8 @@ @article{bieringer2021measuring volume = {10}, pages = {126}, doi = {10.21468/SciPostPhys.10.6.126}, - awesome-category = {application} + awesome-category = {application}, + awesome-tags = {simulation-based; physics; parameter estimation} } @article{bischoff2024practical, @@ -47,7 +51,8 @@ @article{bischoff2024practical year = {2024}, journal = {Transactions on Machine Learning Research}, issn = {2835-8856}, - awesome-category = {review} + awesome-category = {method}, + awesome-tags = {diagnostics; model evaluation} } @misc{cannon2022investigating, @@ -59,7 +64,8 @@ @misc{cannon2022investigating primaryclass = {stat}, publisher = {arXiv}, archiveprefix = {arXiv}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {diagnostics; misspecification; simulation-based} } @article{cranmer2020frontier, @@ -86,7 +92,8 @@ @article{dax2023neural pages = {171403}, doi = {10.1103/PhysRevLett.130.171403}, awesome-category = {method}, - awesome-link-paper = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.171403} + awesome-link-paper = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.171403}, + awesome-tags = {likelihood-based; physics; parameter estimation} } @article{dingeldein2024amortized, @@ -96,7 +103,8 @@ @article{dingeldein2024amortized journal = {bioRxiv : the preprint server for biology}, pages = {2024.07.23.604154}, doi = {10.1101/2024.07.23.604154}, - awesome-category = {application} + awesome-category = {application}, + awesome-tags = {biology; simulation-based; parameter estimation} } @article{elsemuller2024deep, @@ -123,6 +131,7 @@ @article{elsemuller2024sensitivityaware awesome-tldr = {Proposes a framework for amortized and thus efficient sensitivity analyses on all major dimensions of a Bayesian model.}, awesome-link-paper = {https://openreview.net/forum?id=Kxtpa9rvM0}, awesome-link-code = {https://github.com/bayesflow-org/SA-ABI}, + awesome-tags = {sensitivity analysis; simulation-based; meta learning} } @inproceedings{falkiewicz2023calibrating, @@ -130,7 +139,8 @@ @inproceedings{falkiewicz2023calibrating booktitle = {Thirty-Seventh Conference on Neural Information Processing Systems}, author = {Falkiewicz, Maciej and Takeishi, Naoya and Shekhzadeh, Imahn and Wehenkel, Antoine and Delaunoy, Arnaud and Louppe, Gilles and Kalousis, Alexandros}, year = {2023}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {diagnostics; simulation-based} } @inproceedings{foster2021deep, @@ -141,7 +151,7 @@ @inproceedings{foster2021deep volume = {139}, publisher = {PMLR}, awesome-category = {method}, - awesome-tags = {BED; adaptive design} + awesome-tags = {experimental design; adaptive design} } @article{ghaderi-kangavari2023general, @@ -155,7 +165,7 @@ @article{ghaderi-kangavari2023general issn = {2522-0861, 2522-087X}, doi = {10.1007/s42113-023-00167-4}, awesome-category = {application}, - awesome-tags = {cognitive modeling}, + awesome-tags = {cognitive modeling; simulation-based; parameter estimation}, } @misc{habermann2024amortized, @@ -167,7 +177,7 @@ @misc{habermann2024amortized publisher = {arXiv}, archiveprefix = {arXiv}, awesome-category = {method}, - awesome-tags = {parameter estimation} + awesome-tags = {parameter estimation; hierarchical models; simulation-based} } @article{heringhaus2022reliable, @@ -181,6 +191,7 @@ @article{heringhaus2022reliable issn = {1424-8220}, doi = {10.3390/s22145408}, awesome-category = {application}, + awesome-tags = {parameter estimation; simulation-based; engineering} } @misc{lavin2022simulation, @@ -217,7 +228,7 @@ @inproceedings{moon2023amortized publisher = {Association for Computing Machinery}, doi = {10.1145/3544548.3581439}, awesome-category = {application}, - awesome-tags = {human-computer interaction} + awesome-tags = {human-computer interaction, simulation-based; user interfaces} } @article{noever-castelos2022model, @@ -231,7 +242,8 @@ @article{noever-castelos2022model issn = {1095-4244, 1099-1824}, doi = {10.1002/we.2687}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {simulation-based} } @article{papamakarios2021normalizing, @@ -258,7 +270,8 @@ @article{radev2021outbreakflow issn = {1553-7358}, doi = {10.1371/journal.pcbi.1009472}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {epidemiology; public health; simulation-based} } @article{radev2020bayesflow, @@ -272,7 +285,8 @@ @article{radev2020bayesflow pages = {1452--1466}, issn = {2162-237X, 2162-2388}, doi = {10.1109/TNNLS.2020.3042395}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {simulation-based; summary learning; parameter estimation} } @article{radev2023amortized, @@ -285,7 +299,8 @@ @article{radev2023amortized pages = {4903--4917}, issn = {2162-237X, 2162-2388}, doi = {10.1109/TNNLS.2021.3124052}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {model comparison} } @article{radev2023bayesflow, @@ -313,7 +328,8 @@ @inproceedings{radev2023jana volume = {216}, pages = {1695--1706}, publisher = {PMLR}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {joint learning; simulation-based; diagnostics} } @article{sainsbury-dale2024likelihoodfree, @@ -328,7 +344,7 @@ @article{sainsbury-dale2024likelihoodfree doi = {10.1080/00031305.2023.2249522}, awesome-category = {method}, langid = {english}, - awesome-tags = {parameter estimation} + awesome-tags = {parameter estimation; point estimation; simulation-based} } @misc{schmitt2023fuse, @@ -340,7 +356,8 @@ @misc{schmitt2023fuse eprint = {2311.10671}, publisher = {arXiv}, archiveprefix = {arXiv}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {summary learning; parameter estimation} } @inproceedings{schmitt2024amortized, @@ -349,7 +366,8 @@ @inproceedings{schmitt2024amortized author = {Schmitt, Marvin and Li, Chengkun and Vehtari, Aki and Acerbi, Luigi and B{\"u}rkner, Paul-Christian and Radev, Stefan T.}, year = {2024}, publisher = {arXiv}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {likelihood-based; workflow} } @inproceedings{schmitt2024consistency, @@ -357,7 +375,8 @@ @inproceedings{schmitt2024consistency booktitle = {Proceedings of the 38th International Conference on Neural Information Processing Systems}, author = {Schmitt, Marvin and Pratz, Valentin and K{\"o}the, Ullrich and B{\"u}rkner, Paul-Christian and Radev, Stefan T.}, year = {2024}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {simulation-based} } @inproceedings{schmitt2024detecting, @@ -369,7 +388,8 @@ @inproceedings{schmitt2024detecting publisher = {Springer Nature Switzerland}, address = {Cham}, awesome-category = {method}, - isbn = {978-3-031-54605-1} + isbn = {978-3-031-54605-1}, + awesome-tags = {diagnostics; workflow} } @inproceedings{schmitt2024leveraging, @@ -382,7 +402,8 @@ @inproceedings{schmitt2024leveraging volume = {235}, pages = {43723--43741}, publisher = {PMLR}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {likelihood-based; simulation-based} } @article{schumacher2023neural, @@ -396,7 +417,8 @@ @article{schumacher2023neural issn = {2045-2322}, doi = {10.1038/s41598-023-40278-3}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {simulation-based; dynamic modeling; parameter estimation} } @inproceedings{sharrock2024sequential, @@ -409,7 +431,8 @@ @inproceedings{sharrock2024sequential volume = {235}, pages = {44565--44602}, publisher = {PMLR}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {simulation-based} } @article{shiono2021estimation, @@ -422,7 +445,8 @@ @article{shiono2021estimation issn = {01651889}, doi = {10.1016/j.jedc.2021.104082}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {agent modeling; simulation-based} } @article{siahkoohi2023reliable, @@ -436,7 +460,8 @@ @article{siahkoohi2023reliable issn = {0016-8033, 1942-2156}, doi = {10.1190/geo2022-0472.1}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {physics; correction; misspecification} } @misc{starostin2024fast, @@ -448,7 +473,8 @@ @misc{starostin2024fast primaryclass = {cond-mat, physics:physics, stat}, publisher = {arXiv}, archiveprefix = {arXiv}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {physics; meta learning} } @article{tejero-cantero2020sbi, @@ -476,7 +502,8 @@ @inproceedings{tsilifis2022inverse doi = {10.2514/6.2022-0631}, awesome-category = {application}, isbn = {978-1-62410-631-6}, - langid = {english} + langid = {english}, + awesome-tags = {engineering; aerospace; simulation-based} } @article{vonkrause2022mental, @@ -490,7 +517,8 @@ @article{vonkrause2022mental issn = {2397-3374}, doi = {10.1038/s41562-021-01282-7}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {cognitive modeling; parameter estimation} } @inproceedings{ward2022robust, @@ -498,7 +526,8 @@ @inproceedings{ward2022robust booktitle = {Proceedings of the 36th International Conference on Neural Information Processing Systems}, author = {Ward, Daniel and Cannon, Patrick and Beaumont, Mark and Fasiolo, Matteo and Schmon, Sebastian M.}, year = {2022}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {model evaluation; simulation-based} } @article{wang2024missing, @@ -515,7 +544,8 @@ @article{wang2024missing awesome-category = {method}, awesome-tldr = {Encoding missing data in a time series by augmenting the data vector with binary indicators for presence or absence yields the most robust performance.}, awesome-link-paper = {https://doi.org/10.1371/journal.pcbi.1012184}, - awesome-link-code = {https://github.com/emune-dev/Data-missingness-paper} + awesome-link-code = {https://github.com/emune-dev/Data-missingness-paper}, + awesome-tags = {missing data; simulation-based; parameter estimation} } @inproceedings{wehenkel2024simulationbased, @@ -523,7 +553,8 @@ @inproceedings{wehenkel2024simulationbased booktitle = {{{NeurIPS}} Workshop}, author = {Wehenkel, Antoine and Behrmann, Jens and Miller, Andrew C. and Sapiro, Guillermo and Sener, Ozan and Cameto, Marco Cuturi and Jacobsen, J{\"o}rn-Henrik}, year = {2024}, - awesome-category = {application} + awesome-category = {application}, + awesome-tags = {medicine; simulation-based; parameter estimation} } @inproceedings{wildberger2023flow, @@ -534,7 +565,8 @@ @inproceedings{wildberger2023flow year = {2023}, volume = {36}, pages = {16837--16864}, - awesome-category = {method} + awesome-category = {method}, + awesome-tags = {simulation-based} } @article{zeng2023probabilistic, @@ -548,7 +580,8 @@ @article{zeng2023probabilistic issn = {2190-5452, 2190-5479}, doi = {10.1007/s13349-022-00638-5}, awesome-category = {application}, - langid = {english} + langid = {english}, + awesome-tags = {structural health monitoring} } @inproceedings{zhou2024evaluating, @@ -556,5 +589,6 @@ @inproceedings{zhou2024evaluating booktitle = {38th {{Conference}} on {{Neural Information Processing Systems}}}, author = {Zhou, Lingyi and Radev, Stefan T. and Oliver, William H. and Obreja, Aura and Jin, Zehao and Buck, Tobias}, year = {2024}, - awesome-category = {application} + awesome-category = {application}, + awesome-tags = {physics; model evaluation; model comparison} }