Andrew B. Duncan

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 28

Top Topics

Monte Carlo Methods

Hierarchical Bayesian

Multitask Learning

Top Venues

SIAM/ASA J. Uncertain. Quantification

SIAM J. Appl. Dyn. Syst.

Publications

Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew B. Duncan, Mark Girolami
Encoding Domain Expertise into Multilevel Models for Source Location. CoRR (2023)
Xing Liu, Andrew B. Duncan, Axel Gandy
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. ICML (2023)
Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. CoRR (2023)
Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
Training Discrete Energy-Based Models with Energy Discrepancy. CoRR (2023)
Lawrence A. Bull, Domenic Di Francesco, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami
Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning. Comput. Aided Civ. Infrastructure Eng. 38 (7) (2023)
Xing Liu, Andrew B. Duncan, Axel Gandy
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. CoRR (2023)
Kevin H. Huang, Xing Liu, Andrew B. Duncan, Axel Gandy
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. COLT (2023)
Kevin H. Huang, Xing Liu, Andrew B. Duncan, Axel Gandy
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. CoRR (2023)
Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification 11 (4) (2023)
Oliver R. A. Dunbar, Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. SIAM J. Appl. Dyn. Syst. 21 (2) (2022)
Chun Yui Wong, Pranay Seshadri, Andrew B. Duncan, Ashley Scillitoe, Geoffrey T. Parks
Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations. CoRR (2022)
L. A. Bull, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami
Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning. CoRR (2022)
Bryn Noel Ubald, Pranay Seshadri, Andrew B. Duncan
Density Estimation from Schlieren Images through Machine Learning. CoRR (2022)
George Wynne, Andrew B. Duncan
A Kernel Two-Sample Test for Functional Data. J. Mach. Learn. Res. 23 (2022)
Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan
Batch Bayesian Optimization via Particle Gradient Flows. CoRR (2022)
Jonathan Cockayne, Andrew B. Duncan
Probabilistic Gradients for Fast Calibration of Differential Equation Models. SIAM/ASA J. Uncertain. Quantification 9 (4) (2021)
Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
Theoretical Guarantees for the Statistical Finite Element Method. CoRR (2021)
Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. CoRR (2021)
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. MCQMC (2020)
Jon Cockayne, Andrew B. Duncan
Probabilistic Gradients for Fast Calibration of Differential Equation Models. CoRR (2020)
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Andrew B. Duncan, Geoffrey T. Parks
Blade Envelopes Part I: Concept and Methodology. CoRR (2020)
Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. CoRR (2020)
Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey T. Parks, Mark Girolami
Bayesian Assessments of Aeroengine Performance. CoRR (2020)
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Bryn Noel Ubald, Andrew B. Duncan, Geoffrey T. Parks
Blade Envelopes Part II: Multiple Objectives and Inverse Design. CoRR (2020)
Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
Minimum Stein Discrepancy Estimators. CoRR (2019)
Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
Minimum Stein Discrepancy Estimators. NeurIPS (2019)
François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami
Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR (2019)
Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey
Measuring Sample Quality with Diffusions. CoRR (2016)
Andrew B. Duncan
Homogenization of Lateral Diffusion on a Random Surface. Multiscale Model. Simul. 13 (4) (2015)
Andrew B. Duncan, Charles M. Elliott, Grigorios A. Pavliotis, Andrew M. Stuart
A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces. J. Nonlinear Sci. 25 (2) (2015)