Deep Gaussian process regression

May 13, 2021 — May 13, 2021

Gaussian process layer cake.

Figure 1

1 Platonic ideal

TBD.

2 Approximation with dropout

See NN ensembles.

3 References

Cutajar, Bonilla, Michiardi, et al. 2017. Random Feature Expansions for Deep Gaussian Processes.” In PMLR.
Damianou, and Lawrence. 2013. Deep Gaussian Processes.” In Artificial Intelligence and Statistics.
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Dunlop, Girolami, Stuart, et al. 2018. How Deep Are Deep Gaussian Processes? Journal of Machine Learning Research.
Dutordoir, Hensman, van der Wilk, et al. 2021. Deep Neural Networks as Point Estimates for Deep Gaussian Processes.” In arXiv:2105.04504 [Cs, Stat].
Gal, and Ghahramani. 2015. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.” In Proceedings of the 33rd International Conference on Machine Learning (ICML-16).
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Lee, Bahri, Novak, et al. 2018. Deep Neural Networks as Gaussian Processes.” In ICLR.
Leibfried, Dutordoir, John, et al. 2022. A Tutorial on Sparse Gaussian Processes and Variational Inference.”
Mattos, Dai, Damianou, et al. 2017. Deep Recurrent Gaussian Processes for Outlier-Robust System Identification.” Journal of Process Control, DYCOPS-CAB 2016,.
Molchanov, Ashukha, and Vetrov. 2017. Variational Dropout Sparsifies Deep Neural Networks.” In Proceedings of ICML.
Noack, Luo, and Risser. 2023. A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes.”
Ritter, Kukla, Zhang, et al. 2021. Sparse Uncertainty Representation in Deep Learning with Inducing Weights.” arXiv:2105.14594 [Cs, Stat].
Salimbeni, and Deisenroth. 2017. Doubly Stochastic Variational Inference for Deep Gaussian Processes.” In Advances In Neural Information Processing Systems.