Embarrassingly Parallel Inference for Gaussian Processes
This work addresses scalability issues for researchers and practitioners using Gaussian processes in machine learning, though it is incremental as it builds on existing mixture-of-experts approaches.
The paper tackles the computational bottleneck of Gaussian process models, which typically require O(N^3) operations for matrix inversion, by proposing an embarrassingly parallel mixture-of-experts algorithm that achieves comparable performance to Gaussian process regression at a much lower computational cost.
Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix inversion problem cannot adequately model all types of latent functions, and are often not parallelizable. However, judicious choice of model structure can ameliorate this problem. A mixture-of-experts model that uses a mixture of $K$ Gaussian processes offers modeling flexibility and opportunities for scalable inference. Our embarrassingly parallel algorithm combines low-dimensional matrix inversions with importance sampling to yield a flexible, scalable mixture-of-experts model that offers comparable performance to Gaussian process regression at a much lower computational cost.