---
# System prepended metadata

title: '[1-Pager] Challenges of Type-II ML Estimator for Gaussian Processes Brainstorm - 4/2/2021'

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# [1-Pager] Challenges of Type-II ML Estimator for Gaussian Processes Brainstorm - 4/2/2021

## Overview
Topics include:
* What is the model selection problem for GPs?
* Type-II ML Method and derivation
* Consistency in Infinite Regime
* Lack of Properties in Finite Regime
* Challenges of Type-II ML and Approaches To Solve Them

## Motivation and Problem
* Gaussian Processes (GPs) are a powerful non-parametric framework for preforming Bayesian inference by leveraging canonical properties of the Multivariate Normal distribution. 
* Before preforming inference, practitioners need to specify the kernel or covariance function and its corresponding hyperparameters. 
* The correct choice of the kernel hyperparameters and the noise variance parameter directly influences the effectiveness of noisy Gaussian Process inference model. 
* Due to its closed, tractable form and straightforward approach, maximizing the marginal likelihood, Type-II ML has become the classic approach for model selection. 
* However, despite being widely-adopted, Type-II ML, has a handful of detriments. 
* We categorize and review the various challenges of Type-II ML and approaches that attempt to address these issues.

## Model Setup
Suppose we have data $\mathcal{D}:= \{\textbf{x}_i, y_i\}_{i = 1}^n$. We consider $x_i \in \mathbb{R}^d, y_i \in \mathbb{R}, \forall i$.

We are interested in estimating a regression function $\eta(x)$ and model it using a Gaussian Process prior distribution. This is our following \textit{noisy GP model}:

\begin{equation}
\begin{split}
    y_i = \eta(x_i) + \epsilon_i, i = 1, ..., n \\
    \epsilon_i \sim \mathcal{N}(0, \sigma_n^2) \\
    \eta(\cdot) \sim GP(\mu(\cdot), K_{\theta}(\cdot, \cdot))
\end{split}
\end{equation}

We assume that $\eta(\cdot)$ is independent of $\sigma_n^2$ and $\epsilon_i, \forall i.$

Notice $K_{\theta}$ is the covariance matrix constructed by a kernel function $k_{\theta}(x, x')$ depending on hyperparameters $\theta$. We are then able to preform inference by leveraging conditioning properties of multi-variate Gaussians.

Our main objective is choosing $\theta$.

## Challenges of Type-II ML
In the finite data regime, Type-II ML suffers from:
* Overfitting to the training data
* Overconfident uncertainty quantification
* Optimization Difficulty