# Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression

arXiv: https://arxiv.org/abs/2107.08265 · HTML: https://mlsanity.com/paper/2107.08265/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 17 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [Doubly Stochastic Variational Inference for Deep Gaussian Processes](https://mlsanity.com/paper/1705.08933/) (2017)
- [Deep Gaussian Processes for Regression using Approximate Expectation Propagation](https://mlsanity.com/paper/1602.04133/) (2016)
- [Batched Gaussian Process Bandit Optimization via Determinantal Point Processes](https://mlsanity.com/paper/1611.04088/) (2016)
- [Random Feature Expansions for Deep Gaussian Processes](https://mlsanity.com/paper/1610.04386/) (2016)
- [Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1806.05490/) (2018)
- [Variational Auto-encoded Deep Gaussian Processes](https://mlsanity.com/paper/1511.06455/) (2015)
- [Implicit Posterior Variational Inference for Deep Gaussian Processes](https://mlsanity.com/paper/1910.11998/) (2019)
- [Nested Variational Compression in Deep Gaussian Processes](https://mlsanity.com/paper/1412.1370/) (2014)

## 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.