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Enhancing sampling with learned transport maps

By Marylou Gabrié

Appears in collection : Analysis and simulations of metastable systems / Analyse et simulation de systèmes métastables

Deep generative models parametrize very flexible families of distributions able to fit complicated datasets of images or text. These models provide independent samples from complex high-distributions at negligible costs. On the other hand, sampling exactly a target distribution, such as Boltzmann distributions and Bayesian posteriors is typically challenging: either because of dimensionality, multi-modality, ill-conditioning or a combination of the previous. In this talk, I will review recent works trying to enhance traditional inference and sampling algorithms with learning. I will present in particular flowMC, an adaptive MCMC with Normalizing Flows along with first applications and remaining challenges.

Information about the video

Date of recording 03/04/2023
Date of publication 14/04/2023
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Jean Petit
Format MP4

Citation data

DOI 10.24350/CIRM.V.20027903
Cite this video Gabrié, Marylou (03/04/2023). Enhancing sampling with learned transport maps. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.20027903
URL https://dx.doi.org/10.24350/CIRM.V.20027903

Domain(s)

Machine Learning

Bibliography

GABRIÉ, Marylou, ROTSKOFF, Grant M., et VANDEN-EIJNDEN, Eric. Adaptive Monte Carlo augmented with normalizing flows. Proceedings of the National Academy of Sciences, 2022, vol. 119, no 10, p. e2109420119. - https://doi.org/10.1073/pnas.2109420119
SAMSONOV, Sergey, LAGUTIN, Evgeny, GABRIÉ, Marylou, et al. Local-Global MCMC kernels: the best of both worlds. Advances in Neural Information Processing Systems, 2022, vol. 35, p. 5178-5193. - https://proceedings.neurips.cc/paper_files/paper/2022/hash/21c86d5b10cdc28664ccdadf0a29065a-Abstract-Conference.html
WONG, Kaze WK, GABRIÉ, Marylou, et FOREMAN-MACKEY, Daniel. flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax. Journal of Open Source Software, 8(83), 5021, 2023. - https://doi.org/10.21105/joss.05021
GRENIOUX, Louis, DURMUS, Alain, MOULINES, Éric, et al. On Sampling with Approximate Transport Maps. arXiv preprint arXiv:2302.04763, 2023. - https://doi.org/10.48550/arXiv.2302.04763

MSC codes

Document(s)

https://cloud.enpc.fr/s/kz6NipeHBDQ7X8K

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