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[utterances-bot]

Neural Likelihood Free Inference – List of papers using Neural Networks for Bayesian Likelihood-Free Inference

List of papers using Neural Networks for Bayesian Likelihood-Free Inference

https://neurallikelihoodfreeinference.github.io/

[LoryPack]

Please feel free to comment anything missing or typos. If you want to contribute, check the About page (top right).

[mdnunez]

You could add:

Fengler, A., Govindarajan, L. N., Chen, T., & Frank, M. J. (2021). Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience. Elife, 10, e65074.

Schmitt, M., Bürkner, P. C., Köthe, U., & Radev, S. T. (2022). BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference. arXiv preprint arXiv:2112.08866.

[LoryPack]

Thanks, added them! They will appear shortly

[gknicholls]

Maybe us!

Hanwen Xing, Geoff Nicholls, Jeong (Kate) Lee (2020). Distortion estimates for approximate Bayesian inference. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1208-1217, https://proceedings.mlr.press/v124/xing20b.html

[iphysresearch]

You could check these papers that might be suited:

Simulation-based inference:

• Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. “The Frontier of Simulation-Based Inference.” Proceedings of the National Academy of Sciences 117, no. 48 (December 1, 2020): 30055. https://doi.org/10.1073/pnas.1912789117.
• Gabrié, Marylou, Grant M. Rotskoff, and Eric Vanden-Eijnden. “Adaptive Monte Carlo Augmented with Normalizing Flows.” ArXiv:2105.12603 [Cond-Mat, Physics:Physics], February 7, 2022. http://arxiv.org/abs/2105.12603.
• Lueckmann, Jan-Matthis, Jan Boelts, David Greenberg, Pedro Goncalves, and Jakob Macke. “Benchmarking Simulation-Based Inference.” In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, edited by Arindam Banerjee and Kenji Fukumizu, 130:343–51. Proceedings of Machine Learning Research. PMLR, 2021. https://proceedings.mlr.press/v130/lueckmann21a.html.
• Tejero-Cantero, Alvaro, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, and Jakob H. Macke. “SBI -- A Toolkit for Simulation-Based Inference.” ArXiv:2007.09114 [Cs, q-Bio, Stat], July 22, 2020. http://arxiv.org/abs/2007.09114.

A SOTA work in my fields:

• Dax, Maximilian, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, and Jakob H. Macke. “Group Equivariant Neural Posterior Estimation.” ArXiv Preprint ArXiv:2111.13139, November 2021.

[nrstillman]

Another one for the list:

Conor Durkan, Iain Murray, George Papamakarios. "On Contrastive Learning for Likelihood-free Inference." http://proceedings.mlr.press/v119/durkan20a/durkan20a.pdf

[LoryPack]

Thanks everyone, I have added the things you suggested!

[bharti-ayush]

Potential new entry for the list:

Yuexi Wang and Veronika Ročková, "Adversarial Bayesian Simulation." https://arxiv.org/pdf/2208.12113.pdf

[bharti-ayush]

Found another:

Arnaud Delaunoy et al, "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation". https://arxiv.org/pdf/2208.13624.pdf

[LoryPack]

Thanks, I've added them!

[bkmi]

I made a pull request which added a bunch of work from Amsterdam. :)

[zdhNarsil]

Hi, here are two missing works on LFI:

Neural Approximate Sufficient Statistics for Implicit Models
https://arxiv.org/abs/2010.10079

Unifying Likelihood-free Inference with Black-box Optimization and Beyond
https://arxiv.org/abs/2110.03372

[bharti-ayush]

New preprint for the list:

Learning Robust Statistics for Simulation-based Inference under Model Misspecification
https://arxiv.org/abs/2305.15871