Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

This paper is a broad and accessible survey of the methods we have at our disposal for
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

We propose a general purpose variational inference algorithm that forms a natural
counterpart of gradient descent for optimization. Our method iteratively transports a set of …

This paper is an attempt to bridge the conceptual gaps between researchers working on the
two widely used approaches based on positive definite kernels: Bayesian learning or …

We derive a new discrepancy statistic for measuring differences between two probability
distributions based on combining Stein's identity and the reproducing kernel Hilbert space …

Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have
we begun to develop a rigorous understanding of why it performs so well on difficult …

Gradient-based optimization is the foundation of deep learning and reinforcement learning.
Even when the mechanism being optimized is unknown or not differentiable, optimization …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …