Bibliography

CHLS23

Emma R Cobian, Jonathan D Hauenstein, Fang Liu, and Daniele E Schiavazzi. Adaann: adaptive annealing scheduler for probability density approximation. International Journal for Uncertainty Quantification, 2023.

DSDB16

Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. arXiv preprint arXiv:1605.08803, 2016.

Fri91

Jerome H Friedman. Multivariate adaptive regression splines. The annals of statistics, 19(1):1–67, 1991.

GG84

Stuart Geman and Donald Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on pattern analysis and machine intelligence, pages 721–741, 1984.

GGML15

Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE: masked autoencoder for distribution estimation. In International Conference on Machine Learning, 881–889. PMLR, 2015.

Gra07

Robert B Gramacy. Tgp: an r package for bayesian nonstationary, semiparametric nonlinear regression and design by treed gaussian process models. Journal of Statistical Software, 19:1–46, 2007.

IS15

Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, 448–456. PMLR, 2015.

KD18

Durk P Kingma and Prafulla Dhariwal. Glow: generative flow with invertible 1x1 convolutions. Advances in neural information processing systems, 2018.

KSJ+16

Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. Improved variational inference with inverse autoregressive flow. Advances in neural information processing systems, 29:4743–4751, 2016.

KPB20

Ivan Kobyzev, Simon JD Prince, and Marcus A Brubaker. Normalizing flows: an introduction and review of current methods. IEEE transactions on pattern analysis and machine intelligence, 43(11):3964–3979, 2020.

PNR+21

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. The Journal of Machine Learning Research, 22(1):2617–2680, 2021.

PPM18

George Papamakarios, Theo Pavlakou, and Iain Murray. Masked autoregressive flow for density estimation. 2018. arXiv:1705.07057.

RM15

Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In International conference on machine learning, 1530–1538. PMLR, 2015.

Sobol03

Ilya M Sobol'. Theorems and examples on high dimensional model representation. Reliability Engineering and System Safety, 79(2):187–193, 2003.

V+09

Cédric Villani and others. Optimal transport: old and new. Volume 338. Springer, 2009.

WJ+08

Martin J Wainwright, Michael I Jordan, and others. Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1(1–2):1–305, 2008.

WLS22

Yu Wang, Fang Liu, and Daniele E Schiavazzi. Variational inference with nofas: normalizing flow with adaptive surrogate for computationally expensive models. Journal of Computational Physics, 467:111454, 2022.