Adaptive Sparse Gaussian Process

Abstract

Adaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating. Existing solutions only partially cover these needs. Here, we propose the first adaptive sparse Gaussian Process (GP) able to address all these issues. We first reformulate a variational sparse GP algorithm to make it adaptive through a forgetting factor. Next, to make the model inference as simple as possible, we propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives. As a result, the algorithm presents a fast convergence of the inference process, which allows an efficient model update (with a single inference iteration) even in highly nonstationary environments. Experimental results demonstrate the capabilities of the proposed algorithm and its good performance in modeling the predictive posterior in mean and confidence interval estimation compared to state-of-the-art approaches.

High-Accuracy Prediction of ScAlN Thin Film Dry Etching Using Machine Learning Driven Regression Modeling

“…This research marks a novel advancement in etch recipe tuning, where current studies lack ML applications for ScAlN etching optimization. The present methodology may present scalability issues if the input data have many samples because the computational burden of the method is of the order of N 3 , N being the number of training samples, but this problem has been overcome with the use of sparse Gaussian processes, which dramatically reduce the computational burden of the GP modeling without compromising its accuracy, and these versions admit fully nonlinear adaptive counterparts …”