Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
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Abstract (arXiv)
Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging. Sparse approximations simplify the training but often require optimization over a large number of inducing inputs and their locations across layers. In this paper, we simplify the training by setting the locations to a fixed subset of data and sampling the inducing inputs from a variational distribution. This reduces the trainable parameters and computation cost without significant performance degradations, as demonstrated by our empirical results on regression problems. Our modifications simplify and stabilize DGP training while making it amenable to sampling schemes for setting the inducing inputs.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 17 Jul 2021.
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9 of 17 references.
References · 9
- 2013Gaussian Processes for Big Datacited by 377
- 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
- 2016Deep Gaussian Processes for Regression using Approximate Expectation Propagationcited by 39
- 2016Batched Gaussian Process Bandit Optimization via Determinantal Point Processescited by 33
- 2016Random Feature Expansions for Deep Gaussian Processescited by 32
- 2018Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlocited by 32
- 2015Variational Auto-encoded Deep Gaussian Processescited by 19
- 2019Implicit Posterior Variational Inference for Deep Gaussian Processescited by 17
- 2014Nested Variational Compression in Deep Gaussian Processescited by 5
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