Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
This work addresses the scalability issue for researchers and practitioners using Gaussian processes in large-scale settings, representing an incremental improvement through synergistic combination of existing techniques.
The dissertation tackled the scalability problem of Gaussian processes for large datasets by combining iterative methods and pathwise conditioning, resulting in drastically reduced memory requirements and enabling application to significantly larger amounts of data.
Gaussian processes are a powerful framework for uncertainty-aware function approximation and sequential decision-making. Unfortunately, their classical formulation does not scale gracefully to large amounts of data and modern hardware for massively-parallel computation, prompting many researchers to develop techniques which improve their scalability. This dissertation focuses on the powerful combination of iterative methods and pathwise conditioning to develop methodological contributions which facilitate the use of Gaussian processes in modern large-scale settings. By combining these two techniques synergistically, expensive computations are expressed as solutions to systems of linear equations and obtained by leveraging iterative linear system solvers. This drastically reduces memory requirements, facilitating application to significantly larger amounts of data, and introduces matrix multiplication as the main computational operation, which is ideal for modern hardware.