Nick Mancuso: Happy Scientist Workshop #22: How I learned to stop worrying and love autodiff

Much of modern computational statistics relies upon high
dimensional likelihoods whose gradients can be tedious to
derive and correctly implement. Recent advances in automatic
differentiation (i.e. autodiff) have enabled ultra-high dimensional
objectives to be optimized (e.g., deep learning), yet their use for
statistical settings has received less attention. Here I'll
showcase the utility of a state-of-the-art autodiff library for
Python, JAX. This workshop will introduce the basics of autodiff,
how to leverage GPUs for computation by merely setting a single
flag (i.e. no complicated code), and conclude with a
straightforward implementation of a Poisson regression model.