Baseline¶

Algorithms:

• Inverse Weighted Distances
• Radial Basis Function
• Window Function
• Universal Kriging

Gaussian Processes¶

• Scratch: Gaussian Processes + Jax + Cola
• Library: GPJax + Cola
• Custom Library: TinyGP + Cola + Custom Solver

Scaling I - Hardware¶

• Scratch: KeOPs + Gaussian Processes
• Library: KeOPs + GPyTorch
• Library: KeOps + Nystrom

Scaling II - Algorithm¶

Here, we try to take advantage of algorithm speed-ups

Model Approximations¶

• RBFSampler/Nystrom + Linear SGD
• KeOps GPU + Gaussian Kernel

Sparse Approximations¶

These methods are some speed-ups that can be achieved from using SOTA Gaussian processes.

• Sparse Gaussian Process (SGP)
• Stochastic Variational Gaussian Processes (SVGP)
• KISS-GP
• Deep Kernel Learning

Basis Functions¶

• Deep Kernel Learning
• RFF Gaussian Processes
• Spherical Gaussian Process

Dynamical¶

• Linear Kalman Filter
• Non-Linear Kalman Filter
• Markovian Gaussian Processes

Engineering Tricks¶

Feature Engineering¶

Scaling - MinMaxScaler, StandardScaler

Temporal Features - Coordinates, Cyclic, Splines, Fourier Features, Sinusoidal Features

Spatial Features - Coordinate Transforms (Cartesian, Spherical, Cylindrical), Cycle, Splines, Fourier Features, Spherical Harmonics

Patches vs Radius¶