Andrew B. Duncan

According to our database, Andrew B. Duncan authored at least 36 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Retrieval-augmented reasoning with lean language models.

Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas, Rosie Wood, Penelope Yong, Lionel Tarassenko, Mark Girolami, James Geddes, Andrew B. Duncan

CoRR, August, 2025

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems.

Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer, Andrew B. Duncan

CoRR, August, 2025

Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling.

Paula Cordero-Encinar, Ömer Deniz Akyildiz, Andrew B. Duncan

CoRR, February, 2025

Deep Optimal Sensor Placement for Black Box Stochastic Simulations.

Paula Cordero-Encinar, Tobias Schröder, Peter Yatsyshin, Andrew B. Duncan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Modelling Global Trade with Optimal Transport.

Thomas Gaskin, Marie-Therese Wolfram, Andrew B. Duncan, Güven Demirel

CoRR, 2024

Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces.

Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Theoretical Guarantees for the Statistical Finite Element Method.

Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan

SIAM/ASA J. Uncertain. Quantification, December, 2023

On the geometry of Stein variational gradient descent.

Andrew B. Duncan, Nikolas Nüsken, Lukasz Szpruch

J. Mach. Learn. Res., 2023

Training Discrete Energy-Based Models with Energy Discrepancy.

Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan

CoRR, 2023

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models.

Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan

CoRR, 2023

Encoding Domain Expertise into Multilevel Models for Source Location.

Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew B. Duncan, Mark Girolami

CoRR, 2023

Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning.

Lawrence A. Bull, Domenic Di Francesco, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami

Comput. Aided Civ. Infrastructure Eng., 2023

Energy Discrepancies: A Score-Independent Loss for Energy-Based Models.

Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy.

Xing Liu, Andrew B. Duncan, Axel Gandy

Proceedings of the International Conference on Machine Learning, 2023

A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing.

Kevin Han Huang, Xing Liu, Andrew B. Duncan, Axel Gandy

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Energy-Based Models for Functional Data using Path Measure Tilting.

Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods.

Oliver R. A. Dunbar, Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram

SIAM J. Appl. Dyn. Syst., 2022

A Kernel Two-Sample Test for Functional Data.

George Wynne, Andrew B. Duncan

J. Mach. Learn. Res., 2022

Batch Bayesian Optimization via Particle Gradient Flows.

Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan

CoRR, 2022

Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning.

Lawrence A. Bull, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami

CoRR, 2022

Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations.

Chun Yui Wong, Pranay Seshadri, Andrew B. Duncan, Ashley Scillitoe, Geoffrey T. Parks

CoRR, 2022

F-EBM: Energy Based Learning of Functional Data.

Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan

CoRR, 2022

Density Estimation from Schlieren Images through Machine Learning.

Bryn Noel Ubald, Pranay Seshadri, Andrew B. Duncan

CoRR, 2022

Grassmann Stein Variational Gradient Descent.

Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew B. Duncan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Probabilistic Gradients for Fast Calibration of Differential Equation Models.

Jonathan Cockayne, Andrew B. Duncan

SIAM/ASA J. Uncertain. Quantification, 2021

Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods.

Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram

CoRR, 2021

2020

Blade Envelopes Part II: Multiple Objectives and Inverse Design.

Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Bryn Noel Ubald, Andrew B. Duncan, Geoffrey T. Parks

CoRR, 2020

Bayesian Assessments of Aeroengine Performance.

Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey T. Parks, Mark Girolami

CoRR, 2020

Blade Envelopes Part I: Concept and Methodology.

Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Andrew B. Duncan, Geoffrey T. Parks

CoRR, 2020

Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization.

Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol

Proceedings of the Monte Carlo and Quasi-Monte Carlo Methods, 2020

2019

Statistical Inference for Generative Models with Maximum Mean Discrepancy.

François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami

CoRR, 2019

Minimum Stein Discrepancy Estimators.

Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2016

Hybrid framework for the simulation of stochastic chemical kinetics.

Andrew B. Duncan, Radek Erban, Konstantinos C. Zygalakis

J. Comput. Phys., 2016

Measuring Sample Quality with Diffusions.

Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey

CoRR, 2016

2015

Homogenization of Lateral Diffusion on a Random Surface.

Andrew B. Duncan

Multiscale Model. Simul., 2015

A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces.

Andrew B. Duncan, Charles M. Elliott, Grigorios A. Pavliotis, Andrew M. Stuart

J. Nonlinear Sci., 2015