Your second-worst review is probably all you need
We analyze nine years of ICLR reviews and find that Reviewer 2 is not the problem.
The Upper Confidence Bound in Disguise
We show that UCB is a quantile of the predictive distribution in disguise, elicitable by the pinball loss even though no scalar loss can elicit the moment formula directly. The …
Three eras of my dotfiles
We tour a chezmoi-templated dotfiles setup that bootstraps a fresh machine in one command — terminal, shell, prompt, multiplexer, multi-environment templating, and the developer …
Gaussian Process Regression in 9,000 Google Sheets Cell Formulas
We implement a fully functional Gaussian Process regression pipeline — Cholesky decomposition, posterior predictions, and gradient-based hyperparameter optimization — in pure …
Efficient Cholesky decomposition of low-rank updates
We give a short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates.
A Primer on Pólya-gamma Random Variables - Part III: Local Variational Methods
We swap Gibbs sampling for mean-field variational inference in the Pólya-Gamma augmented model and watch the classical Jaakkola-Jordan bound on the logistic sigmoid fall out, EM …
A Primer on Pólya-gamma Random Variables - Part II: Bayesian Logistic Regression
We use one weird trick — Pólya-Gamma augmentation — to make exact inference in Bayesian logistic regression tractable.
A Primer on Pólya-gamma Random Variables - Part I: Basic Relationships
We collect the identities that make the Pólya-Gamma augmentation tick: the logistic sigmoid in terms of the hyperbolic cosine, the hyperbolic cosine as a Pólya-Gamma Laplace …
An Illustrated Guide to the Knowledge Gradient Acquisition Function
We give a short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.
A Handbook for Sparse Variational Gaussian Processes
We summarize the notation, identities, and derivations underlying the sparse variational Gaussian process (SVGP) framework.