testing a simple extension of adaboost to prevent overfitting

the gentlest possible introduction to generalization error bounds beyond uniform convergence

evaluating some bounds to go from (statistical learning) theory to practice

generalizing linear discriminant analysis beyond normally distributed data

bayesian inference in model misspecification settings, visually explained

a toy bayesian neural network with a precise parameter posterior

never escaping jekyll :)