Heating Up Quasi-Monte Carlo Graph Random Features: A Diffusion Kernel Perspective

Abstract

We build upon a recently introduced class of quasi-graph random features (q-GRFs), which have demonstrated the ability to yield lower variance estimators of the 2-regularized Laplacian kernel (Choromanski 2023). Our research investigates whether similar results can be achieved with alternative kernel functions, specifically the Diffusion (or Heat), Matérn, and Inverse Cosine kernels. We find that the Diffusion kernel performs most similarly to the 2-regularized Laplacian, and we further explore graph types that benefit from the previously established antithetic termination procedure. Specifically, we explore Erdős-Rényi and Barabási-Albert random graph models, Binary Trees, and Ladder graphs, with the goal of identifying combinations of specific kernel and graph type that benefit from antithetic termination. We assert that q-GRFs achieve lower variance estimators of the Diffusion (or Heat) kernel on Ladder graphs. However, the number of rungs on the Ladder graphs impacts the algorithm's performance; further theoretical results supporting our experimentation are forthcoming. This work builds upon some of the earliest Quasi-Monte Carlo methods for kernels defined on combinatorial objects, paving the way for kernel-based learning algorithms and future real-world applications in various domains.