Add Example Notebooks

[jejjohnson]

I would like to add some example notebooks of how we can use JAX with the kernel methods.

Some ideas:

Tutorials

• Kernels and Derivatives with autograd
• Estimating sigma (scott, silverman, median, mean, kth distance)
• Sensitivity Analysis
  • KRR, GPR
  • SVR
  • HSIC
• Uncertain Inputs for GPs

Kernels

• random fourier features (RFF)

Algorithms

• kernel ridge regression (exact, stochastic gradient descent)
• KRR w. RFF
• RV Coefficient (linear multidimensional Pearsons)
• Approximate SVR (RFF + Weights + SGD)
• Approximate GP (RFF + Variational Layer + SGD)
• Optimized / Kernel Entropy Components Analysis (O/KECA)