Normalizing flows are tractable density models that can approximate complicated target
distributions, eg Boltzmann distributions of physical systems. However, current methods for …

We introduce a new mean-field ODE and corresponding interacting particle systems (IPS)
for sampling from an unnormalized target density. The IPS are gradient-free, available in …

We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by
considering a variational definition with respect to a generic mean, thereby extending the …

Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a
path of distributions annealing between a tractable reference and an intractable target, and …

Abstract Annealed Importance Sampling (AIS) synthesizes weighted samples from an
intractable distribution given its unnormalized density function. This algorithm relies on a …

The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree
determined by the parameter λ, with the other distribution. Such divergence is an …

Annealed Importance Sampling (AIS) is one of the most effective methods for marginal
likelihood estimation. It relies on a sequence of distributions interpolating between a …

Computing ratios of normalizing constants plays an important role in statistical modeling.
Two important examples are hypothesis testing in latent variables models, and model …

We propose a new dynamic algorithm which transports samples from a reference
distribution to a target distribution in unit time, given access to the target-to-reference density …

Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes
used to sample complex high-dimensional probability distributions. They rely on a collection …