Francois-Xavier Briol

research

∙ 05/22/2023

Bayesian Numerical Integration with Neural Networks

Bayesian probabilistic numerical methods for numerical integration offer...

0 Katharina Ott, et al. ∙

research

∙ 03/08/2023

Meta-learning Control Variates: Variance Reduction with Limited Data

Control variates can be a powerful tool to reduce the variance of Monte ...

0 Zhuo Sun, et al. ∙

research

∙ 02/09/2023

Robust and Scalable Bayesian Online Changepoint Detection

This paper proposes an online, provably robust, and scalable Bayesian ap...

0 Matías Altamirano, et al. ∙

research

∙ 01/27/2023

Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference

Likelihood-free inference methods typically make use of a distance betwe...

0 Ayush Bharti, et al. ∙

research

∙ 10/15/2022

Multilevel Bayesian Quadrature

Multilevel Monte Carlo is a key tool for approximating integrals involvi...

0 Kaiyu Li, et al. ∙

research

∙ 09/15/2022

Towards Healing the Blindness of Score Matching

Score-based divergences have been widely used in machine learning and st...

5 Mingtian Zhang, et al. ∙

research

∙ 06/16/2022

Generalised Bayesian Inference for Discrete Intractable Likelihood

Discrete state spaces represent a major computational challenge to stati...

22 Takuo Matsubara, et al. ∙

research

∙ 02/09/2022

Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap

Simulator-based models are models for which the likelihood is intractabl...

8 Charita Dellaporta, et al. ∙

research

∙ 12/03/2021

ProbNum: Probabilistic Numerics in Python

Probabilistic numerical methods (PNMs) solve numerical problems via prob...

6 Jonathan Wenger, et al. ∙

research

∙ 11/19/2021

Composite Goodness-of-fit Tests with Kernels

Model misspecification can create significant challenges for the impleme...

0 Oscar Key, et al. ∙

research

∙ 09/18/2021

Vector-Valued Control Variates

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

0 Zhuo Sun, et al. ∙

research

∙ 06/22/2021

Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo

Intractable generative models are models for which the likelihood is una...

0 Ziang Niu, et al. ∙

research

∙ 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. ∙

research

∙ 04/15/2021

Robust Generalised Bayesian Inference for Intractable Likelihoods

Generalised Bayesian inference updates prior beliefs using a loss functi...

9 Takuo Matsubara, et al. ∙

research

∙ 10/16/2020

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks

Bayesian neural networks attempt to combine the strong predictive perfor...

0 Takuo Matsubara, et al. ∙

research

∙ 06/12/2020

Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization

Control variates are a well-established tool to reduce the variance of M...

0 Shijing Si, et al. ∙

research

∙ 06/09/2020

Bayesian Probabilistic Numerical Integration with Tree-Based Models

Bayesian quadrature (BQ) is a method for solving numerical integration p...

1 Harrison Zhu, et al. ∙

research

∙ 01/29/2020

Convergence Guarantees for Gaussian Process Approximations Under Several Observation Models

Gaussian processes are ubiquitous in statistical analysis, machine learn...

34 George Wynne, et al. ∙

research

∙ 08/08/2019

Contributed Discussion of "A Bayesian Conjugate Gradient Method"

We would like to congratulate the authors of "A Bayesian Conjugate Gradi...

0 Francois-Xavier Briol, et al. ∙

research

∙ 06/19/2019

Minimum Stein Discrepancy Estimators

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

2 Alessandro Barp, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

research

∙ 11/26/2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"

This article is the rejoinder for the paper "Probabilistic Integration: ...

18 Francois-Xavier Briol, et al. ∙

research

∙ 03/27/2018

Stein Points

An important task in computational statistics and machine learning is to...

0 Wilson Ye Chen, et al. ∙

research

∙ 01/12/2018

Bayesian Quadrature for Multiple Related Integrals

Bayesian probabilistic numerical methods are a set of tools providing po...

0 Xiaoyue Xi, et al. ∙

research

∙ 06/11/2017

On the Sampling Problem for Kernel Quadrature

The standard Kernel Quadrature method for numerical integration with ran...

0 Francois-Xavier Briol, et al. ∙

research

∙ 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. ∙

research

∙ 12/03/2015

Probabilistic Integration: A Role in Statistical Computation?

A research frontier has emerged in scientific computation, wherein numer...

0 Francois-Xavier Briol, et al. ∙

research

∙ 06/08/2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

There is renewed interest in formulating integration as an inference pro...

0 Francois-Xavier Briol, et al. ∙

Francois-Xavier Briol

Featured Co-authors