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Andrew B. Duncan
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2020 – today
- 2024
- [i21]Thomas Gaskin, Marie-Therese Wolfram, Andrew B. Duncan, Güven Demirel:
Modelling Global Trade with Optimal Transport. CoRR abs/2409.06554 (2024) - 2023
- [j8]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): 821-848 (2023) - [j7]Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification 11(4): 1278-1307 (2023) - [c5]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: 3827-3918 - [c4]Xing Liu, Andrew B. Duncan, Axel Gandy:
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. ICML 2023: 21527-21547 - [i20]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 abs/2302.05686 (2023) - [i19]Xing Liu, Andrew B. Duncan, Axel Gandy:
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. CoRR abs/2304.14762 (2023) - [i18]Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew B. Duncan, Mark Girolami:
Encoding Domain Expertise into Multilevel Models for Source Location. CoRR abs/2305.08657 (2023) - [i17]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 abs/2307.06431 (2023) - [i16]Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan:
Training Discrete Energy-Based Models with Energy Discrepancy. CoRR abs/2307.07595 (2023) - 2022
- [j6]George Wynne, Andrew B. Duncan:
A Kernel Two-Sample Test for Functional Data. J. Mach. Learn. Res. 23: 73:1-73:51 (2022) - [j5]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): 1539-1572 (2022) - [c3]Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew B. Duncan:
Grassmann Stein Variational Gradient Descent. AISTATS 2022: 2002-2021 - [i15]Bryn Noel Ubald, Pranay Seshadri, Andrew B. Duncan:
Density Estimation from Schlieren Images through Machine Learning. CoRR abs/2201.05233 (2022) - [i14]Xing Liu, Harrison Zhu, Jean-François Ton, George Wynne, Andrew B. Duncan:
Grassmann Stein Variational Gradient Descent. CoRR abs/2202.03297 (2022) - [i13]Chun Yui Wong, Pranay Seshadri, Andrew B. Duncan, Ashley Scillitoe, Geoffrey T. Parks:
Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations. CoRR abs/2203.03508 (2022) - [i12]Lawrence 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 abs/2204.12404 (2022) - [i11]Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan:
Batch Bayesian Optimization via Particle Gradient Flows. CoRR abs/2209.04722 (2022) - 2021
- [j4]Jonathan Cockayne, Andrew B. Duncan:
Probabilistic Gradients for Fast Calibration of Differential Equation Models. SIAM/ASA J. Uncertain. Quantification 9(4): 1643-1672 (2021) - [i10]Andrew B. Duncan, Andrew M. Stuart
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. CoRR abs/2104.03384 (2021) - [i9]Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. CoRR abs/2111.07691 (2021) - 2020
- [c2]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: 205-221 - [i8]Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol:
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. CoRR abs/2006.07487 (2020) - [i7]Jon Cockayne
Probabilistic Gradients for Fast Calibration of Differential Equation Models. CoRR abs/2009.04239 (2020) - [i6]Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe
Blade Envelopes Part I: Concept and Methodology. CoRR abs/2011.11636 (2020) - [i5]Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey T. Parks, Mark Girolami:
Bayesian Assessments of Aeroengine Performance. CoRR abs/2011.14698 (2020) - [i4]Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe
Blade Envelopes Part II: Multiple Objectives and Inverse Design. CoRR abs/2012.15579 (2020)
2010 – 2019
- 2019
- [c1]Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. NeurIPS 2019: 12964-12976 - [i3]François-Xavier Briol, Alessandro Barp
Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR abs/1906.05944 (2019) - [i2]Alessandro Barp
Minimum Stein Discrepancy Estimators. CoRR abs/1906.08283 (2019) - 2016
- [j3]Andrew B. Duncan, Radek Erban, Konstantinos C. Zygalakis
Hybrid framework for the simulation of stochastic chemical kinetics. J. Comput. Phys. 326: 398-419 (2016) - [i1]Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey:
Measuring Sample Quality with Diffusions. CoRR abs/1611.06972 (2016) - 2015
- [j2]Andrew B. Duncan, Charles M. Elliott
A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces. J. Nonlinear Sci. 25(2): 389-449 (2015) - [j1]Andrew B. Duncan:
Homogenization of Lateral Diffusion on a Random Surface. Multiscale Model. Simul. 13(4): 1478-1506 (2015)
Coauthor Index
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