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Vanguard documentation
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Vanguard documentation

Examples

  • Introduction
    • Creating a Decorator
  • Regression
    • Introduction to Gaussian Processes
    • Bayesian treatment of hyperparameters with Laplace approximations
    • Distributed GPs
    • Sparse variational inference for GPs
    • Sparse GP regression
  • Classification
    • Binary Classification in Vanguard
    • Multiclass Classification in Vanguard
    • Multiclass Classification with Dirichlet Distributions

Tutorials

  • Creating a Decorator

Components

  • Base GP models
  • Kernels

Controllers

  • Base Controller Class
  • Vanilla Gaussian GP Controller
  • GPs with Input Uncertainty

Decorators

  • Compositionally Warped GPs
    • Existing Warp Functions
    • Creating Warp Functions
  • Learning Likelihood Noise
  • Normalising Inputs
  • Variational Inference
  • Distributed GPs
  • Classification
  • Multitask GPs
  • Decorator Tools
  • Compositional input warping for GPs
  • Hierarchical GPs with Bayesian Hyperparameters
  • Higher Rank Features
  • Disable standard input scaling

Optimisation

  • Smart Optimiser
  • Learning rate finder
  • Learning rate schedulers

Datasets

  • Air Passengers Dataset
  • Base Datasets
  • Bike Dataset
  • Synthetic Data
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Use the following examples to familiarise yourself with Vanguard and its advanced features.

Introduction¶

Creating a Decorator

Regression¶

Introduction to Gaussian Processes
Bayesian treatment of hyperparameters with Laplace approximations
Distributed GPs
Sparse variational inference for GPs
Sparse GP regression

Classification¶

Binary Classification in Vanguard
Multiclass Classification in Vanguard
Multiclass Classification with Dirichlet Distributions
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Creating a Decorator
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