36 citations · 130 across the 21 of their papers we have counts for
6 papers · 1 filter
Statistical Inference for Generative Models with Maximum Mean Discrepancy
Francois-Xavier Briol, Alessandro Barp, Andrew B. Duncan +1
While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving in…
Minimum Stein Discrepancy Estimators
Alessandro Barp, Francois-Xavier Briol, Andrew B. Duncan +2
When maximum likelihood estimation is infeasible, one often turns to score matching, contrastive divergence, or minimum probability flow to obtain tractable parameter estimates. We…
Stein Point Markov Chain Monte Carlo
Wilson Ye Chen, Alessandro Barp, François-Xavier Briol +4
An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a cla…
Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction
Alessandro Barp, Anthony Kennedy, Mark Girolami
The Hamiltonian Monte Carlo method generates samples by introducing a mechanical system that explores the target density. For distributions on manifolds it is not always simple to…
Irreversible Langevin MCMC on Lie Groups
Alexis Arnaudon, Alessandro Barp, So Takao
It is well-known that irreversible MCMC algorithms converge faster to their stationary distributions than reversible ones. Using the special geometric structure of Lie groups $\mat…
Hamiltonian Monte Carlo On Lie Groups and Constrained Mechanics on Homogeneous Manifolds
Alessandro Barp
In this paper we show that the Hamiltonian Monte Carlo method for compact Lie groups constructed in \cite{kennedy88b} using a symplectic structure can be recovered from canonical g…