Théo Galy-Fajou
PhD Student at TU Berlin with Pr. Opper. I am interested in Bayesian methods and more particularly in approximate inference and Gaussian Processes. I am developing AugmentedGaussianProcesses.jl and KernelFunctions.jl
Sessions
Kernel functions are used in an incredible number of applications ranging from SVM to Gaussian processes as well as Stein variational methods.
With KernelFunctions.jl we propose a modular and easily customizable kernel framework. The emphasis made in this package is to work smoothly with automatic differentiation while being able to construct arbitrarily complex kernels both in terms of input transformation and kernel structure.
Gaussian Processes (GP) are an essential model of Bayesian non-parametrics. While multiple GP packages already exist in Julia such as Stheno.jl or GaussianProcesses.jl, AugmentedGaussianProcesses.jl has a larger scope of applications and is constantly updated with state-of-the-art methods. One of its specificity is to work with augmented variables to simplify inference. In this talk I will briefly explain this concept and show the potential of the package.