The recently developed Bayesian Gaussian process latent variable model (GPLVM) is a powerful generative model for discovering low dimensional embeddings in linear time complexity. However, modern datasets are so large that even linear-time models find them difficult to cope with. We introduce a novel re-parametrisation of variational inference for the GPLVM and sparse GP model that allows for an efficient distributed inference algorithm. We present a unifying derivation for both models, analytically deriving the optimal variational distribution over the inducing points. We then assess the suggested inference on datasets of different sizes, showing that it scales well with both data and computational resources. We furthermore demonstrate its practicality in real-world settings using datasets with up to 100 thousand points, comparing the inference to sequential implementations, assessing the distribution of the load among the different nodes, and testing its robustness to network failures.

本论文介绍了一种基于稀疏高斯过程回归和潜变量模型的重新参数化变分推断方法，可以有效解决大规模数据集下高斯过程模型的可扩展性问题，并在飞行数据和MNIST数据集上证明了其优越性。

稀疏高斯过程回归和潜变量模型的分布式变分推断