Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

We advocate an optimization-centric view of Bayesian inference. Our inspiration is the
representation of Bayes' rule as infinite-dimensional optimization (Csisz´ r, 1975; Donsker …

This paper demonstrates how to recover causal graphs from the score of the data
distribution in non-linear additive (Gaussian) noise models. Using score matching …

Deep generative models parametrized up to a normalizing constant (eg energy-based
models) are difficult to train by maximizing the likelihood of the data because the likelihood …

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a
likelihood, and can therefore be used to confer robustness against possible mis …

We advocate an optimization-centric view on and introduce a novel generalization of
Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite …

We present a new method for evaluating and training unnormalized density models. Our
approach only requires access to the gradient of the unnormalized model's log-density. We …

This paper proposes an online, provably robust, and scalable Bayesian approach for
changepoint detection. The resulting algorithm has key advantages over previous work: it …

Score matching is an alternative to maximum likelihood (ML) for estimating a probability
distribution parametrized up to a constant of proportionality. By fitting the''score''of the …

To enable closed form conditioning, a common assumption in Gaussian process (GP)
regression is independent and identically distributed Gaussian observation noise. This …