Walkthrough of the derivation of streaming sparse Gaussian processes [Bui et al., 2017]
Tutorial on Conjugate-Computation Variational Inference (CVI). A computationally efficient, modular, and parameter efficient generalization of variational inference
Tutorial on variational Gaussian approximation and why using Gaussian priors and factorizing likelihoods leads to only $O(N)$ instead of $O(N^2)$ variational parameters, $N$ being the number of random variables
Tutorial on the natural parameterization of the exponential-family distributions and how it leads to computationally efficient natural gradient descent in conjugate models
Walkthrough of the derivation of the variational sparse Gaussian processes [Titsias 2009]
A flow chart showing key Sparse Gaussian process methods and how they relate to each other.
A flow chart showing my process to effectively grasp the content of an academic paper
A flow chart showing various SGMCMC methods in machine learning and how they relate to each other
Gaussian processes are one of the dominant approaches in Bayesian learning. This tutorial explains Gaussian processes with interactive figures and code
Algorithms to simultaneously compute the optimal assignments and formation parameters for a team of robots from a given initial formation to a variable goal formation (where the shape of the goal formation is given, and its scale and location parameters must be optimized)
