116 citations · 415 across the 17 of their papers we have counts for
22 papers
Flexible model composition in machine learning and its implementation in MLJ
Anthony D. Blaom, Sebastian J. Vollmer
A graph-based protocol called `learning networks' which combine assorted machine learning models into meta-models is described. Learning networks are shown to overcome several limi…
Foundations of Bayesian Learning from Synthetic Data
Harrison Wilde, Jack Jewson, Sebastian Vollmer +1
There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy…
Debiasing classifiers: is reality at variance with expectation?
Ashrya Agrawal, Florian Pfisterer, Bernd Bischl +5
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse r…
Model updating after interventions paradoxically introduces bias
James Liley, Samuel R Emerson, Bilal A Mateen +3
Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Rece…
A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services
Harrison Wilde, Lucia Lushi Chen, Austin Nguyen +7
Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-…
MLJ: A Julia package for composable machine learning
Anthony D. Blaom, Franz Kiraly, Thibaut Lienart +3
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages. It…
Design choices for productive, secure, data-intensive research at scale in the cloud
Diego Arenas, Jon Atkins, Claire Austin +22
We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk prof…
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner +15
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing s…
The True Cost of Stochastic Gradient Langevin Dynamics
Tigran Nagapetyan, Andrew B. Duncan, Leonard Hasenclever +3
The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been proposed to obtain asymptotically correct sam…
Note on A. Barbour's paper on Stein's method for diffusion approximations
Mikolaj J. Kasprzak, Andrew B. Duncan, Sebastian J. Vollmer
In (Barbour, 1990) foundations for diffusion approximation via Stein's method are laid. This paper has been cited more than 130 times and is a cornerstone in the area of Stein's me…
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet +5
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how th…
Multilevel Monte Carlo for Scalable Bayesian Computations
Mike Giles, Tigran Nagapetyan, Lukasz Szpruch +2
Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations. However, they need to access the full data set in order to evaluate the posterior density at eve…
Relativistic Monte Carlo
Xiaoyu Lu, Valerio Perrone, Leonard Hasenclever +2
Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Ha…
Multilevel Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
Michael B. Giles, Mateusz B. Majka, Lukasz Szpruch +2
We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles2015) to calculate expectations with respect to the invariant measure of an ergod…
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart +4
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to e…
An iterative technique for bounding derivatives of solutions of Stein equations
Christian Döbler, Robert E. Gaunt, Sebastian J. Vollmer
We introduce a simple iterative technique for bounding derivatives of solutions of Stein equations , where is a linear differential operator and is the…
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
Alexandre Bouchard-Côté, Sebastian J. Vollmer, Arnaud Doucet
Markov chain Monte Carlo methods have become standard tools in statistics to sample from complex probability measures. Many available techniques rely on discrete-time reversible Ma…
(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
Sebastian J. Vollmer, Konstantinos C. Zygalakis, and Yee Whye Teh
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally infeasible. The recently proposed stochastic gradient Langevin dynamics (SGLD) me…
Consistency and fluctuations for stochastic gradient Langevin dynamics
Yee Whye Teh, Alexandre Thiéry, Sebastian Vollmer
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive. Both the calculation of the acceptance probability and the creation of…
Posterior Consistency for Bayesian Inverse Problems through Stability and Regression Results
Sebastian J. Vollmer
In the Bayesian approach, the a priori knowledge about the input of a mathematical model is described via a probability measure. The joint distribution of the unknown input and the…
Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors
Sebastian J. Vollmer
The computational complexity of MCMC methods for the exploration of complex probability measures is a challenging and important problem. A challenge of particular importance arises…
Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
Martin Hairer, Andrew M. Stuart, Sebastian J. Vollmer
We study the problem of sampling high and infinite dimensional target measures arising in applications such as conditioned diffusions and inverse problems. We focus on those that a…