12 citations · 38 across the 9 of their papers we have counts for
9 papers
Robust and Scalable Bayesian Online Changepoint Detection
Matias Altamirano, François-Xavier Briol, Jeremias Knoblauch
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides…
Multilevel Bayesian Quadrature
Kaiyu Li, Daniel Giles, Toni Karvonen +2
Multilevel Monte Carlo is a key tool for approximating integrals involving expensive scientific models. The idea is to use approximations of the integrand to construct an estimator…
ProbNum: Probabilistic Numerics in Python
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner +9
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential…
Composite Goodness-of-fit Tests with Kernels
Oscar Key, Arthur Gretton, François-Xavier Briol +1
Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly…
Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
Andreas Anastasiou, Alessandro Barp, François-Xavier Briol +11
Stein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin…
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Takuo Matsubara, Chris J. Oates, François-Xavier Briol
Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated with the predictive output in…
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Shijing Si, Chris. J. Oates, Andrew B. Duncan +2
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample setting…
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Harrison Zhu, Xing Liu, Ruya Kang +3
Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows users to quantify their uncertainty about the solution. The stand…
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
George Wynne, François-Xavier Briol, Mark Girolami
Gaussian processes are ubiquitous in machine learning, statistics, and applied mathematics. They provide a flexible modelling framework for approximating functions, whilst simultan…