Alessandro Barp | DeepAI

DeepAI AI Chat

Featured Co-authors

Michael I. Jordan
283 publications
Qiang Liu
190 publications
Arthur Gretton
87 publications
Mark Girolami
61 publications
Lester Mackey
60 publications
Chris J. Oates
46 publications
Francois-Xavier Briol
29 publications
Bruno Ebner
26 publications
Gesine Reinert
25 publications
Karl Friston
24 publications
Andrew B. Duncan
20 publications

∙ 08/16/2023

Warped geometric information on the optimisation of Euclidean functions

We consider the fundamental task of optimizing a real-valued function de...

0 Marcelo Hartmann, et al. ∙

∙ 09/26/2022

Targeted Separation and Convergence with Kernel Discrepancies

Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD...

0 Alessandro Barp, et al. ∙

∙ 03/20/2022

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents

In this chapter, we identify fundamental geometric structures that under...

9 Alessandro Barp, et al. ∙

∙ 09/18/2021

Vector-Valued Control Variates

Control variates are post-processing tools for Monte Carlo estimators wh...

0 Zhuo Sun, et al. ∙

∙ 07/23/2021

Optimization on manifolds: A symplectic approach

There has been great interest in using tools from dynamical systems and ...

7 Guilherme França, et al. ∙

∙ 05/07/2021

Stein's Method Meets Statistics: A Review of Some Recent Developments

Stein's method is a collection of tools for analysing distributional com...

0 Andreas Anastasiou, et al. ∙

∙ 05/06/2021

A Unifying and Canonical Description of Measure-Preserving Diffusions

A complete recipe of measure-preserving diffusions in Euclidean space wa...

0 Alessandro Barp, et al. ∙

∙ 06/16/2020

Metrizing Weak Convergence with Maximum Mean Discrepancies

Theorem 12 of Simon-Gabriel Schölkopf (JMLR, 2018) seemed to close a...

0 Carl-Johann Simon-Gabriel, et al. ∙

∙ 06/19/2019

Minimum Stein Discrepancy Estimators

When maximum likelihood estimation is infeasible, one often turns to sco...

2 Alessandro Barp, et al. ∙

∙ 06/13/2019

Statistical Inference for Generative Models with Maximum Mean Discrepancy

While likelihood-based inference and its variants provide a statisticall...

0 Francois-Xavier Briol, et al. ∙

∙ 05/09/2019

Stein Point Markov Chain Monte Carlo

An important task in machine learning and statistics is the approximatio...

12 Wilson Ye Chen, et al. ∙

∙ 03/21/2019

Irreversible Langevin MCMC on Lie Groups

It is well-known that irreversible MCMC algorithms converge faster to th...

0 Alexis Arnaudon, et al. ∙

∙ 03/07/2019

Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction

The Hamiltonian Monte Carlo method generates samples by introducing a me...

0 Alessandro Barp, et al. ∙

∙ 10/11/2018

A Riemannian-Stein Kernel Method

This paper presents a theoretical analysis of numerical integration base...

0 Alessandro Barp, et al. ∙

∙ 12/05/2017

Posterior Integration on a Riemannian Manifold

The geodesic Markov chain Monte Carlo method and its variants enable com...

0 Chris J. Oates, et al. ∙

∙ 05/08/2017

Geometry and Dynamics for Markov Chain Monte Carlo

Markov Chain Monte Carlo methods have revolutionised mathematical comput...

0 Alessandro Barp, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world