@inproceedings{kirichenko2020why,
	title        = {Why Normalizing Flows Fail to Detect Out-of-Distribution Data},
	author       = {Kirichenko, Polina and Izmailov, Pavel and Wilson, Andrew Gordon},
	year         = 2020,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{dai2020sliced,
	title        = {Sliced Iterative Normalizing Flows},
	author       = {Biwei Dai and Uros Seljak},
	year         = 2021,
	booktitle    = {ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models}
}
@inproceedings{nalisnick2018deep,
	title        = {Do Deep Generative Models Know What They Don't Know?},
	author       = {Eric Nalisnick and Akihiro Matsukawa and Yee Whye Teh and Dilan Gorur and Balaji Lakshminarayanan},
	year         = 2019,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{klein2021funnels,
	title        = {Funnels: Exact maximum likelihood with dimensionality reduction},
	author       = {Samuel Klein and John A. Raine and Sebastian Pina{-}Otey and Slava Voloshynovskiy and Tobias Golling},
	year         = 2021,
	booktitle    = {Workshop on Bayesian Deep Learning, Advances in Neural Information Processing Systems}
}
@inproceedings{nielsen2020survae,
	title        = {SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows},
	author       = {Nielsen, Didrik and Jaini, Priyank and Hoogeboom, Emiel and Winther, Ole and Welling, Max},
	year         = 2020,
	booktitle      = {Advances in Neural Information Processing Systems}
}
@book{brooks2011handbook,
	title        = {Handbook of Markov Chain Monte Carlo},
	author       = {Brooks, Steve and Gelman, Andrew and Jones, Galin and Meng, Xiao-Li},
	year         = 2011,
	publisher    = {CRC press}
}
@article{wainwright2008graphical,
	title        = {Graphical Models, Exponential Families, and Variational Inference},
	author       = {Martin J. Wainwright and Michael I. Jordan},
	year         = 2008,
	journal      = {Foundations and Trends in Machine Learning},
	volume       = 1,
	number       = {1–2},
	pages        = {1--305}
}
@book{sisson2018handbook,
	title        = {Handbook of Approximate Bayesian Computation},
	author       = {Sisson, Scott A and Fan, Yanan and Beaumont, Mark},
	year         = 2018,
	publisher    = {CRC Press}
}
@article{beaumont2009adaptive,
	title        = {Adaptive Approximate {B}ayesian Computation},
	author       = {Beaumont, Mark A and Cornuet, Jean-Marie and Marin, Jean-Michel and Robert, Christian P},
	year         = 2009,
	journal      = {Biometrika},
	publisher    = {Oxford University Press},
	volume       = 96,
	number       = 4,
	pages        = {983--990}
}
@inproceedings{papamakarios2019sequential,
	title        = {Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows},
	author       = {Papamakarios, George and Sterratt, David and Murray, Iain},
	year         = 2019,
	booktitle    = {Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics}
}
@inproceedings{greenberg2019automatic,
	title        = {Automatic Posterior Transformation for Likelihood-Free Inference},
	author       = {Greenberg, David and Nonnenmacher, Marcel and Macke, Jakob},
	year         = 2019,
	booktitle    = {Proceedings of the 36th International Conference on Machine Learning}
}
@inproceedings{hermans2020likelihood,
	title        = {Likelihood-free {MCMC} with Amortized Approximate Ratio Estimators},
	author       = {Hermans, Joeri and Begy, Volodimir and Louppe, Gilles},
	year         = 2020,
	booktitle    = {Proceedings of the 37th International Conference on Machine Learning}
}
@inproceedings{durkan2020contrastive,
	title        = {On Contrastive Learning for Likelihood-free Inference},
	author       = {Durkan, Conor and Murray, Iain and Papamakarios, George},
	year         = 2020,
	booktitle    = {Proceedings of the 37th International Conference on Machine Learning}
}
@inproceedings{miller2022contrastive,
	title        = {Contrastive Neural Ratio Estimation},
	author       = {Benjamin Kurt Miller and Christoph Weniger and Patrick Forr{\'e}},
	year         = 2022,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{delaunoy2022towards,
	title        = {Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation},
	author       = {Delaunoy, Arnaud and Hermans, Joeri and Rozet, Fran\c{c}ois and Wehenkel, Antoine and Louppe, Gilles},
	year         = 2022,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{papamakarios2016fast,
	title        = {Fast $\varepsilon$-free Inference of Simulation Models with {B}ayesian Conditional Density Estimation},
	author       = {Papamakarios, George and Murray, Iain},
	year         = 2016,
	booktitle    = {Advances in Neural Information Processing Systems},	
}
@article{cranmer2020frontier,
	title        = {The frontier of simulation-based inference},
	author       = {Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles},
	year         = 2020,
	journal      = {Proceedings of the National Academy of Sciences},
	publisher    = {National Acad Sciences},
	volume       = 117,
	number       = 48,
	pages        = {30055--30062}
}
@inproceedings{papamakarios2017masked,
	title        = {Masked Autoregressive Flow for Density Estimation},
	author       = {Papamakarios, George and Pavlakou, Theo and Murray, Iain},
	year         = 2017,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{germain2015made,
	title        = {MADE: Masked Autoencoder for Distribution Estimation},
	author       = {Germain, Mathieu and Gregor, Karol and Murray, Iain and Larochelle, Hugo},
	year         = 2015,
	booktitle    = {Proceedings of the 32nd International Conference on Machine Learning}
}
@article{papamakarios2021normalizing,
	title        = {Normalizing flows for probabilistic modeling and inference},
	author       = {Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji},
	year         = 2021,
	journal      = {The Journal of Machine Learning Research},	
	volume       = 22,
	number       = 1,
	pages        = {2617--2680}
}
@article{fefferman2016testing,
	title        = {Testing the manifold hypothesis},
	author       = {Fefferman, Charles and Mitter, Sanjoy and Narayanan, Hariharan},
	year         = 2016,
	journal      = {Journal of the American Mathematical Society},
	volume       = 29,
	number       = 4,
	pages        = {983--1049}
}
@article{neal2003slice,
	title        = {Slice Sampling},
	author       = {Neal, Radford M},
	year         = 2003,
	journal      = {The Annals of Statistics},
	publisher    = {Institute of Mathematical Statistics},
	volume       = 31,
	number       = 3,
	pages        = {705--767}
}
@inproceedings{glockler2022variational,
	title        = {Variational methods for simulation-based inference},
	author       = {Manuel Gl{\"o}ckler and Michael Deistler and Jakob H. Macke},
	year         = 2022,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{deistler2022truncated,
	title        = {Truncated proposals for scalable and hassle-free simulation-based inference},
	author       = {Michael Deistler and Pedro J. Goncalves and Jakob H. Macke},
	year         = 2022,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{dinh2016density,
	title        = {Density estimation using Real {NVP}},
	author       = {Laurent Dinh and Jascha Sohl-Dickstein and Samy Bengio},
	year         = 2017,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{dinh2014nice,
	title        = {{NICE:} Non-linear Independent Components Estimation},
	author       = {Laurent Dinh and David Krueger and Yoshua Bengio},
	year         = 2015,
	booktitle    = {Workshop Track, International Conference on Learning Representations}
}
@article{belisle1993hit,
	title        = {Hit-and-Run Algorithms for Generating Multivariate Distributions},
	author       = {B{\'e}lisle, Claude JP and Romeijn, H Edwin and Smith, Robert L},
	year         = 1993,
	journal      = {Mathematics of Operations Research},
	publisher    = {INFORMS},
	volume       = 18,
	number       = 2,
	pages        = {255--266}
}
@article{dillon2017tensorflow,
	title        = {Tensorflow distributions},
	author       = {Dillon, Joshua V and Langmore, Ian and Tran, Dustin and Brevdo, Eugene and Vasudevan, Srinivas and Moore, Dave and Patton, Brian and Alemi, Alex and Hoffman, Matt and Saurous, Rif A},
	year         = 2017,
	journal      = {arXiv preprint arXiv:1711.10604}
}
@article{tejero2020sbi,
	title        = {sbi: A toolkit for simulation-based inference},
	author       = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
	year         = 2020,
	journal      = {Journal of Open Source Software},
	publisher    = {The Open Journal},
	volume       = 5,
	number       = 52,
	pages        = 2505
}
@article{albert2022learning,
	title        = {{Learning summary statistics for {B}ayesian inference with autoencoders}},
	author       = {Albert, Carlo and Ulzega, Simone and Ozdemir, Firat and Perez-Cruz, Fernando and Mira, Antonietta},
	year         = 2022,
	journal      = {SciPost Physics Core},
	volume       = 5,
	number       = 3,
	pages        = {043}
}
@article{albert2021can,
	title        = {Can Stochastic Resonance Explain Recurrence of Grand Minima?},
	author       = {Albert, Carlo and Ferriz-Mas, Antonio and Gaia, Filippo and Ulzega, Simone},
	year         = 2021,
	journal      = {The Astrophysical Journal Letters},
	publisher    = {IOP Publishing},
	volume       = 916,
	number       = 2,
	pages        = {L9}
}
@article{gretton2012kernel,
	title        = {A kernel two-sample test},
	author       = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
	year         = 2012,
	journal      = {The Journal of Machine Learning Research},
	volume       = 13,
	number       = 1,
	pages        = {723--773}
}
@article{brehmer2021simulation,
	title        = {Simulation-based inference in particle physics},
	author       = {Brehmer, Johann},
	year         = 2021,
	journal      = {Nature Reviews Physics},
	publisher    = {Nature Publishing Group UK London},
	volume       = 3,
	number       = 5,
	pages        = {305--305}
}
@inproceedings{delaunoy2020lightning,
	title        = {Lightning-fast gravitational wave parameter inference through neural amortization},
	author       = {Delaunoy, Arnaud and Wehenkel, Antoine and Hinderer, Tanja and Nissanke, Samaya and Weniger, Christoph and Williamson, Andrew R and Louppe, Gilles},
	year         = 2020,
	booktitle    = {Workshop on Machine Learning and the Physical Sciences, Advances in Neural Information Processing Systems}
}
@article{dax2021real,
	title        = {Real-Time Gravitational Wave Science with Neural Posterior Estimation},
	author       = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Macke, Jakob H. and Buonanno, Alessandra and Sch\"olkopf, Bernhard},
	year         = 2021,
	journal      = {Phys. Rev. Lett.},
	publisher    = {American Physical Society},
	volume       = 127,
	pages        = 241103,
	issue        = 24,
	numpages     = 7
}
@article{ratmann2007using,
	title        = {Using likelihood-free inference to compare evolutionary dynamics of the protein networks of {H}. pylori and {P}. falciparum},
	author       = {Ratmann, Oliver and J{\o}rgensen, Ole and Hinkley, Trevor and Stumpf, Michael and Richardson, Sylvia and Wiuf, Carsten},
	year         = 2007,
	journal      = {PLoS Computational Biology},
	publisher    = {Public Library of Science San Francisco, USA},
	volume       = 3,
	number       = 11,
	pages        = {e230}
}
@article{hermans2021towards,
	title        = {Towards constraining warm dark matter with stellar streams through neural simulation-based inference},
	author       = {Hermans, Joeri and Banik, Nilanjan and Weniger, Christoph and Bertone, Gianfranco and Louppe, Gilles},
	year         = 2021,
	journal      = {Monthly Notices of the Royal Astronomical Society},
	publisher    = {Oxford University Press},
	volume       = 507,
	number       = 2
}
@article{pritchard1999population,
	title        = {Population growth of human Y chromosomes: a study of Y chromosome microsatellites},
	author       = {Pritchard, Jonathan K and Seielstad, Mark T and Perez-Lezaun, Anna and Feldman, Marcus W},
	year         = 1999,
	journal      = {Molecular Biology and Evolution},
	publisher    = {Oxford University Press},
	volume       = 16,
	number       = 12,
	pages        = {1791--1798}
}
@article{cranmer2015approximating,
	title        = {Approximating likelihood ratios with calibrated discriminative classifiers},
	author       = {Cranmer, Kyle and Pavez, Juan and Louppe, Gilles},
	year         = 2015,
	journal      = {arXiv preprint arXiv:1506.02169}
}
@article{thomas2022likelihood,
	title        = {Likelihood-free inference by ratio estimation},
	author       = {Thomas, Owen and Dutta, Ritabrata and Corander, Jukka and Kaski, Samuel and Gutmann, Michael U},
	year         = 2022,
	journal      = {Bayesian Analysis},
	publisher    = {International Society for Bayesian Analysis},
	volume       = 17,
	number       = 1,
	pages        = {1--31}
}
@inproceedings{lueckmann2017flexible,
	title        = {Flexible statistical inference for mechanistic models of neural dynamics},
	author       = {Lueckmann, Jan-Matthis and Goncalves, Pedro J and Bassetto, Giacomo and \"{O}cal, Kaan and Nonnenmacher, Marcel and Macke, Jakob H},
	year         = 2017,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{lueckmann2021benchmarking,
	title        = {Benchmarking Simulation-Based Inference},
	author       = {Lueckmann, Jan-Matthis and Boelts, Jan and Greenberg, David and Goncalves, Pedro and Macke, Jakob},
	year         = 2021,
	booktitle    = {Proceedings of the 24th International Conference on Artificial Intelligence and Statistics}
}
@article{brehmer2018constraining,
	title        = {Constraining Effective Field Theories with Machine Learning},
	author       = {Brehmer, Johann and Cranmer, Kyle and Louppe, Gilles and Pavez, Juan},
	year         = 2018,
	journal      = {Phys. Rev. Lett.},
	publisher    = {American Physical Society},
	volume       = 121,
	pages        = 111801,
	issue        = 11,
	numpages     = 5
}
@article{albert2015simulated,
	title        = {A simulated annealing approach to approximate {B}ayes computations},
	author       = {Albert, Carlo and K{\"u}nsch, Hans R and Scheidegger, Andreas},
	year         = 2015,
	journal      = {Statistics and Computing},
	publisher    = {Springer},
	volume       = 25,
	pages        = {1217--1232}
}
@article{hermans2021averting,
	title        = {A Crisis In Simulation-Based Inference? {B}eware, Your Posterior Approximations Can Be Unfaithful},
	author       = {Joeri Hermans and Arnaud Delaunoy and Fran{\c{c}}ois Rozet and Antoine Wehenkel and Volodimir Begy and Gilles Louppe},
	year         = 2022,
	journal      = {Transactions on Machine Learning Research}
}
@inproceedings{durkan2019neural,
	title        = {Neural Spline Flows},
	author       = {Durkan, Conor and Bekasov, Artur and Murray, Iain and Papamakarios, George},
	year         = 2019,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@article{wiqvist2021sequential,
	title        = {Sequential neural posterior and likelihood approximation},
	author       = {Wiqvist, Samuel and Frellsen, Jes and Picchini, Umberto},
	year         = 2021,
	journal      = {arXiv preprint arXiv:2102.06522}
}
@article{betancourt2017conceptual,
	title        = {A conceptual introduction to {H}amiltonian {M}onte {C}arlo},
	author       = {Betancourt, Michael},
	year         = 2017,
	journal      = {arXiv preprint arXiv:1701.02434}
}
@inproceedings{cunningham2020normalizing,
	title        = {Normalizing flows across dimensions},
	author       = {Cunningham, Edmond and Zabounidis, Renos and Agrawal, Abhinav and Fiterau, Madalina and Sheldon, Daniel},
	year         = 2020,
	booktitle    = {ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models}
}
@software{jax2018github,
	title        = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
	author       = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
	year         = 2018,
	url          = {http://github.com/google/jax},
	version      = {0.3.13}
}
@article{charbonneau2005fluctuations,
	title        = {Fluctuations in {B}abcock-{L}eighton dynamos. I. period doubling and transition to chaos},
	author       = {Charbonneau, Paul and St-Jean, C{\'e}dric and Zacharias, Pia},
	year         = 2005,
	journal      = {The Astrophysical Journal},
	publisher    = {IOP Publishing},
	volume       = 619,
	number       = 1,
	pages        = 613
}
@software{deepmind2020jax,
	title        = {The {D}eep{M}ind {JAX} {E}cosystem},
	author       = {Babuschkin, Igor and Baumli and others},
	year         = 2020,
	url          = {http://github.com/deepmind}
}
@article{gelman1992inference,
	title        = {Inference from Iterative Simulation Using Multiple Sequences},
	author       = {Andrew Gelman and Donald B. Rubin},
	year         = 1992,
	journal      = {Statistical Science},
	volume       = 7,
	number       = 4,
	pages        = {457 -- 472}
}
@article{gabry2019visualization,
	title        = {Visualization in Bayesian workflow},
	author       = {Gabry, Jonah and Simpson, Daniel and Vehtari, Aki and Betancourt, Michael and Gelman, Andrew},
	year         = 2019,
	journal      = {Journal of the Royal Statistical Society Series A: Statistics in Society},
	publisher    = {Oxford University Press},
	volume       = 182,
	number       = 2,
	pages        = {389--402}
}
@inproceedings{kingma2015adam,
	title        = {Adam: {A} Method for Stochastic Optimization},
	author       = {Diederik P. Kingma and Jimmy Ba},
	year         = 2015,
	booktitle    = {International Conference on Learning Representations}
}
@article{del2012adaptive,
	title        = {An adaptive sequential {M}onte {C}arlo method for approximate {B}ayesian computation},
	author       = {Del Moral, Pierre and Doucet, Arnaud and Jasra, Ajay},
	year         = 2012,
	journal      = {Statistics and Computing},
	publisher    = {Springer},
	volume       = 22,
	pages        = {1009--1020}
}
@inproceedings{zhao2022comparing,
	title        = {Comparing distributions by measuring differences that affect decision making},
	author       = {Zhao, Shengjia and Sinha, Abhishek and He, Yutong and Perreault, Aidan and Song, Jiaming and Ermon, Stefano},
	year         = 2022,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{ho2019flow,
	title        = {Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design},
	author       = {Ho, Jonathan and Chen, Xi and Srinivas, Aravind and Duan, Yan and Abbeel, Pieter},
	year         = 2019,
	booktitle    = {Proceedings of the 36th International Conference on Machine Learning}
}
@inproceedings{Theis2016a,
	title        = {A note on the evaluation of generative models},
	author       = {L. Theis and A. van den Oord and M. Bethge},
	year         = 2016,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{rodrigues2021hnpe,
	title        = {{HNPE}: Leveraging Global Parameters for Neural Posterior Estimation},
	author       = {Rodrigues, Pedro and Moreau, Thomas and Louppe, Gilles and Gramfort, Alexandre},
	year         = 2021,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@article{buckwar2020spectral,
	title        = {Spectral density-based and measure-preserving {ABC} for partially observed diffusion processes. An illustration on {H}amiltonian {SDE}s},
	author       = {Buckwar, Evelyn and Tamborrino, Massimiliano and Tubikanec, Irene},
	year         = 2020,
	journal      = {Statistics and Computing},
	publisher    = {Springer},
	volume       = 30,
	pages        = {627--648}
}
@inproceedings{radev2023jana,
	title        = {{JANA}: Jointly Amortized Neural Approximation of Complex Bayesian Models},
	author       = {Stefan T. Radev and Marvin Schmitt and Valentin Pratz and Umberto Picchini and Ullrich Koethe and Paul Buerkner},
	year         = 2023,
	booktitle    = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}
}
@article{ableidinger2017stochastic,
	title        = {A stochastic version of the {J}ansen and {R}it neural mass model: analysis and numerics},
	author       = {Ableidinger, Markus and Buckwar, Evelyn and Hinterleitner, Harald},
	year         = 2017,
	journal      = {The Journal of Mathematical Neuroscience},
	publisher    = {SpringerOpen},
	volume       = 7,
	number       = 1,
	pages        = {1--35}
}
@inproceedings{linhart2023lcst,
	title        = {{L}-{C}2{ST}: Local Diagnostics for Posterior Approximations in Simulation-Based Inference},
	author       = {Julia Linhart and Alexandre Gramfort and Pedro L. C. Rodrigues},
	year         = 2023,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{geffner2023compositional,
	title        = {Compositional Score Modeling for Simulation-Based Inference},
	author       = {Geffner, Tomas and Papamakarios, George and Mnih, Andriy},
	year         = 2023,
	booktitle    = {Proceedings of the 40th International Conference on Machine Learning}
}
@inproceedings{jia2024simulation,
	title        = {Simulation-Based Inference with Quantile Regression},
	author       = {Jia, He},
	year         = 2024,
	booktitle    = {Proceedings of the 41st International Conference on Machine Learning}
}
@inproceedings{schmitt2023consistency,
	title        = {Consistency Models for Scalable and Fast Simulation-Based Inference},
	author       = {Schmitt, Marvin and Pratz, Valentin and K\"{o}the, Ullrich and B\"{u}rkner, Paul-Christian and Radev, Stefan T.},
	year         = 2024,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{simons2023neural,
	title        = {Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models},
	author       = {Jack Simons and Louis Sharrock and Song Liu and Mark Beaumont},
	year         = 2023,
	booktitle    = {Fifth Symposium on Advances in Approximate Bayesian Inference}
}
@article{glaser2022maximum,
	title        = {Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference},
	author       = {Glaser, Pierre and Arbel, Michael and Doucet, Arnaud and Gretton, Arthur},
	year         = 2022,
	journal      = {arXiv preprint arXiv:2210.14756}
}
@inproceedings{miller2023simulationbased,
	title        = {Simulation-based Inference with the Generalized {K}ullback-{L}eibler Divergence},
	author       = {Benjamin Kurt Miller and Marco Federici and Christoph Weniger and Patrick Forr{\'e}},
	year         = 2023,
	booktitle    = {ICML 2023 Workshop on the Synergy of Scientific and Machine Learning Modeling}
}
@inproceedings{wildberger2023flow,
	title        = {Flow Matching for Scalable Simulation-Based Inference},
	author       = {Jonas Bernhard Wildberger and Maximilian Dax and Simon Buchholz and Stephen R Green and Jakob H. Macke and Bernhard Sch{\"o}lkopf},
	year         = 2023,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{beck2022efficient,
	title        = {Efficient identification of informative features in simulation-based inference},
	author       = {Jonas Beck and Michael Deistler and Yves Bernaerts and Jakob H. Macke and Philipp Berens},
	year         = 2022,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{yao2023discriminative,
	title        = {Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier},
	author       = {Yuling Yao and Justin Domke},
	year         = 2023,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@article{alsing2019nuisance,
	title        = {Generalized massive optimal data compression},
	author       = {Alsing, Justin and Wandelt, Benjamin},
	year         = 2018,
	journal      = {Monthly Notices of the Royal Astronomical Society: Letters},
	publisher    = {Oxford University Press},
	volume       = 476,
	number       = 1,
	pages        = {L60--L64}
}
@inproceedings{chen2021neural,
	title        = {Neural Approximate Sufficient Statistics for Implicit Models},
	author       = {Yanzhi Chen and Dinghuai Zhang and Michael U. Gutmann and Aaron Courville and Zhanxing Zhu},
	year         = 2021,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{yao2023simulation,
	title        = {Simulation-Based Stacking},
	author       = {Yao, Yuling and R\'{e}galdo-Saint Blancard, Bruno and Domke, Justin},
	year         = 2024,
	booktitle    = {Proceedings of the 27th International Conference on Artificial Intelligence and Statistics}
}
@article{talts2018validating,
	title        = {Validating {B}ayesian inference algorithms with simulation-based calibration},
	author       = {Talts, Sean and Betancourt, Michael and Simpson, Daniel and Vehtari, Aki and Gelman, Andrew},
	year         = 2018,
	journal      = {arXiv preprint arXiv:1804.06788}
}
@inproceedings{zhao2021diagnostics,
	title        = {Diagnostics for conditional density models and {B}ayesian inference algorithms},
	author       = {Zhao, David and Dalmasso, Niccol{\`o} and Izbicki, Rafael and Lee, Ann B},
	year         = 2021,
	booktitle    = {Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence}
}
@inproceedings{gruner2023pseudolikelihood,
	title        = {Pseudo-Likelihood Inference},
	author       = {Theo Gruner and Boris Belousov and Fabio Muratore and Daniel Palenicek and Jan Peters},
	year         = 2023,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{lopezpaz2017revisiting,
	title        = {Revisiting Classifier Two-Sample Tests},
	author       = {David Lopez-Paz and Maxime Oquab},
	year         = 2017,
	booktitle    = {International Conference on Learning Representations}
}
@inproceedings{sutherland2017generative,
	title        = {Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy},
	author       = {Danica J. Sutherland and Hsiao-Yu Tung and Heiko Strathmann and Soumyajit De and Aaditya Ramdas and Alex Smola and Arthur Gretton},
	year         = 2017,
	booktitle    = {International Conference on Learning Representations}
}
@book{sarkka2019applied,
	title        = {Applied stochastic differential equations},
	author       = {S{\"a}rkk{\"a}, Simo and Solin, Arno},
	year         = 2019,
	publisher    = {Cambridge University Press}
}
@software{haiku2020github,
	title        = {{H}aiku: {S}onnet for {JAX}},
	author       = {Tom Hennigan and Trevor Cai and Tamara Norman and Lena Martens and Igor Babuschkin},
	year         = 2020,
	url          = {http://github.com/google-deepmind/dm-haiku}
}
@article{hoffman2014no,
  author  = {Matthew D. Hoffman and Andrew Gelman},
  title   = {The {N}o-{U}-{T}urn Sampler: Adaptively Setting Path Lengths in {H}amiltonian {M}onte {C}arlo},
  journal = {Journal of Machine Learning Research},
  year    = {2014},
  volume  = {15},
  number  = {47},
  pages   = {1593--1623}
}
@software{blackjax2020github,
	title        = {{B}lackjax: A sampling library for {JAX}},
	author       = {Cabezas, Alberto and Lao, Junpeng and Louf, R\'emi},
	year         = 2023,
	url          = {http://github.com/blackjax-devs/blackjax}
}
@phdthesis{kidger2021on,
	title        = {{O}n {N}eural {D}ifferential {E}quations},
	author       = {Patrick Kidger},
	year         = 2021,
	school       = {University of Oxford}
}
@inproceedings{chen2018cnf,
	title        = {Neural Ordinary Differential Equations},
	author       = {Chen, Ricky T. Q. and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David K},
	year         = 2018,
	booktitle    = {Advances in Neural Information Processing Systems}
}
@inproceedings{grathwohl2019scalable,
	title        = {FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models},
	author       = {Grathwohl, Will and Chen, Ricky TQ and Bettencourt, Jesse and Duvenaud, David},
	year         = 2019,
	booktitle    = {International Conference on Learning Representations}
}
@article{ferrari2004beta,
	title        = {Beta regression for modelling rates and proportions},
	author       = {Ferrari, Silvia and Cribari-Neto, Francisco},
	year         = 2004,
	journal      = {Journal of Applied Statistics},
	publisher    = {Taylor \& Francis},
	volume       = 31,
	number       = 7,
	pages        = {799--815}
}
@article{forbes2022summary,
	title        = {Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors},
	author       = {Forbes, Florence and Nguyen, Hien Duy and Nguyen, TrungTin and Arbel, Julyan},
	year         = 2022,
	journal      = {Statistics and Computing},
	publisher    = {Springer},
	volume       = 32,
	number       = 5,
	pages        = 85
}
@article{vehtari2021rhat,
	title        = {{Rank-Normalization, Folding, and Localization: An Improved $\widehat{R}$ for Assessing Convergence of MCMC}},
	author       = {Aki Vehtari and Andrew Gelman and Daniel Simpson and Bob Carpenter and Paul-Christian B{\"u}rkner},
	year         = 2021,
	journal      = {Bayesian Analysis},
	volume       = 16,
	number       = 2,
	pages        = {667 -- 718}
}
@article{amsaleg2022intrinsic,
	title        = {Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis},
	author       = {Amsaleg, Laurent and Chelly, Oussama and Houle, Michael E and Kawarabayashi, Ken-ichi and Radovanovi{\'c}, Milo{\v{s}} and Treeratanajaru, Weeris},
	year         = 2022,
	journal      = {arXiv preprint arXiv:2209.14475}
}
@article{bac2021scikit,
	title        = {Scikit-dimension: a python package for intrinsic dimension estimation},
	author       = {Bac, Jonathan and Mirkes, Evgeny M and Gorban, Alexander N and Tyukin, Ivan and Zinovyev, Andrei},
	year         = 2021,
	journal      = {Entropy},
	volume       = 23,
	number       = 10,
	pages        = 1368
}
@article{lenormand2013adaptive,
	title        = {Adaptive approximate {B}ayesian computation for complex models},
	author       = {Lenormand, Maxime and Jabot, Franck and Deffuant, Guillaume},
	year         = 2013,
	journal      = {Computational Statistics},
	publisher    = {Springer},
	volume       = 28,
	number       = 6,
	pages        = {2777--2796}
}
@inproceedings{kingma2018glow,
	title        = {Glow: Generative flow with invertible 1x1 convolutions},
	author       = {Kingma, Durk P and Dhariwal, Prafulla},
	year         = 2018,
	booktitle      = {Advances in Neural Information Processing Systems}
}
@article{hoffmann2022minimizing,
	title        = {Minimizing the Expected Posterior Entropy Yields Optimal Summary Statistics},
	author       = {Hoffmann, Till and Onnela, Jukka-Pekka},
	year         = 2022,
	journal      = {arXiv preprint arXiv:2206.02340}
}
@inproceedings{chen2023learning,
  title = 	 {Is Learning Summary Statistics Necessary for Likelihood-free Inference?},
  author =       {Chen, Yanzhi and Gutmann, Michael U. and Weller, Adrian},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},  
  year = 	 {2023},
}
@article{pacchiardi2022score,
	title        = {Score matched neural exponential families for likelihood-free inference},
	author       = {Pacchiardi, Lorenzo and Dutta, Ritabrata},
	year         = 2022,
	journal      = {The Journal of Machine Learning Research},
	volume       = 23,
	number       = 1,
	pages        = {1745--1815}
}
@article{gonccalves2020training,
	title        = {Training deep neural density estimators to identify mechanistic models of neural dynamics},
	author       = {Gon{\c{c}}alves, Pedro J and Lueckmann, Jan-Matthis and Deistler, Michael and Nonnenmacher, Marcel and {\"O}cal, Kaan and Bassetto, Giacomo and Chintaluri, Chaitanya and Podlaski, William F and Haddad, Sara A and Vogels, Tim P and others},
	year         = 2020,
	journal      = {Elife},
	publisher    = {eLife Sciences Publications, Ltd},
	volume       = 9,
	pages        = {e56261}
}
@article{dirmeier2024simulation,
	title        = {Simulation-based inference with the {P}ython Package sbijax},
	author       = {Dirmeier, Simon and Ulzega, Simone and Mira, Antonietta and Albert, Carlo},
	year         = 2024,
	journal      = {arXiv preprint arXiv:2409.19435}
}
@article{dirmeier2024surjectors,
	title        = {Surjectors: surjection layers for density estimation with normalizing flows},
	author       = {Dirmeier, Simon},
	year         = 2024,
	journal      = {Journal of Open Source Software},
	volume       = 9,
	number       = 94,
	pages        = 6188
}
@inproceedings{lipman2023flow,
	title        = {Flow Matching for Generative Modeling},
	author       = {Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matthew Le},
	year         = 2023,
	booktitle    = {The Eleventh International Conference on Learning Representations}
}
@inproceedings{sharrock2024sequential,
	title        = {Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models},
	author       = {Louis Sharrock and Jack Simons and Song Liu and Mark Beaumont},
	year         = 2024,
	booktitle    = {Proceedings of the 41st International Conference on Machine Learning}
}
@inproceedings{cao2020block,
	title        = {Block Neural Autoregressive Flow},
	author       = {{De Cao}, Nicola and Aziz, Wilker and Titov, Ivan},
	year         = 2020,
	booktitle    = {Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence}
}
@inproceedings{huang2018neural,
	title        = {Neural Autoregressive Flows},
	author       = {Huang, Chin-Wei and Krueger, David and Lacoste, Alexandre and Courville, Aaron},
	year         = 2018,
	booktitle    = {Proceedings of the 35th International Conference on Machine Learning}
}
@inproceedings{kingma2016improved,
	title        = {Improved Variational Inference with Inverse Autoregressive Flow},
	author       = {Kingma, Durk P and Salimans, Tim and Jozefowicz, Rafal and Chen, Xi and Sutskever, Ilya and Welling, Max},
	year         = 2016,
	booktitle    = {Advances in Neural Information Processing Systems}
}
