Andrew B. Duncan

52 papers A* 6A 3Journal 42Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Batch Bayesian Optimization via Particle Gradient Flows. SIAM/ASA J. Uncertain. Quantification Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan
2026	J	`jnl`	Diffusion Path Samplers via Sequential Monte Carlo. CoRR James Matthew Young, Paula Cordero-Encinar, Sebastian Reich, Andrew B. Duncan, Ömer Deniz Akyildiz
2025	J	`jnl`	Certified Self-Consistency: Statistical Guarantees and Test-Time Training for Reliable Reasoning in LLMs. CoRR Paula Cordero-Encinar, Andrew B. Duncan
2025	A	`conf`	Deep Optimal Sensor Placement for Black Box Stochastic Simulations. AISTATS Paula Cordero-Encinar, Tobias Schröder, Peter Yatsyshin, Andrew B. Duncan
2025	J	`jnl`	Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling. CoRR Paula Cordero-Encinar, Ömer Deniz Akyildiz, Andrew B. Duncan
2025	J	`jnl`	Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems. CoRR Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer, Andrew B. Duncan
2025	J	`jnl`	Retrieval-augmented reasoning with lean language models. CoRR Ryan Sze-Yin Chan, Federico Nanni, Tomas Lazauskas, Rosie Wood, Penelope Yong, Lionel Tarassenko, Mark Girolami, James Geddes, Andrew B. Duncan
2025	A*	`conf`	Robust and Conjugate Spatio-Temporal Gaussian Processes. ICML William Laplante, Matías Altamirano, Andrew B. Duncan, Jeremias Knoblauch, François-Xavier Briol
2025	J	`jnl`	Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities. CoRR Takuo Matsubara, Andrew B. Duncan, Simon L. Cotter, Konstantinos Zygalakis
2025	J	`jnl`	Uniform-in-time convergence bounds for Persistent Contrastive Divergence Algorithms. CoRR Paul Felix Valsecchi Oliva, Ömer Deniz Akyildiz, Andrew B. Duncan
2024	J	`jnl`	Deep Optimal Sensor Placement for Black Box Stochastic Simulations. CoRR Paula Cordero-Encinar, Tobias Schröder, Peter Yatsyshin, Andrew B. Duncan
2024	A*	`conf`	Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. NeurIPS Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
2024	J	`jnl`	Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. CoRR Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
2024	J	`jnl`	Modelling Global Trade with Optimal Transport. CoRR Thomas Gaskin, Marie-Therese Wolfram, Andrew B. Duncan, Güven Demirel
2023	A*	`conf`	A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. COLT Kevin Han Huang, Xing Liu, Andrew B. Duncan, Axel Gandy
2023	J	`jnl`	A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing. CoRR Kevin Han Huang, Xing Liu, Andrew B. Duncan, Axel Gandy
2023	J	`jnl`	Encoding Domain Expertise into Multilevel Models for Source Location. CoRR Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew B. Duncan, Mark Girolami
2023	A*	`conf`	Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. NeurIPS Tobias Schröder, Zijing Ou, Jen Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
2023	J	`jnl`	Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. CoRR Tobias Schröder, Zijing Ou, Jen Ning Lim, Yingzhen Li, Sebastian J. Vollmer, Andrew B. Duncan
2023	A	`conf`	Energy-Based Models for Functional Data using Path Measure Tilting. AISTATS Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan
2023	J	`jnl`	Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning. Comput. Aided Civ. Infrastructure Eng. Lawrence A. Bull, Domenic Di Francesco, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami
2023	J	`jnl`	On the geometry of Stein variational gradient descent. J. Mach. Learn. Res. Andrew B. Duncan, Nikolas Nüsken, Lukasz Szpruch
2023	J	`jnl`	Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
2023	J	`jnl`	Training Discrete Energy-Based Models with Energy Discrepancy. CoRR Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan
2023	A*	`conf`	Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. ICML Xing Liu, Andrew B. Duncan, Axel Gandy
2023	J	`jnl`	Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. CoRR Xing Liu, Andrew B. Duncan, Axel Gandy
2022	J	`jnl`	A Kernel Two-Sample Test for Functional Data. J. Mach. Learn. Res. George Wynne, Andrew B. Duncan
2022	J	`jnl`	Batch Bayesian Optimization via Particle Gradient Flows. CoRR Enrico Crovini, Simon L. Cotter, Konstantinos Zygalakis, Andrew B. Duncan
2022	J	`jnl`	Density Estimation from Schlieren Images through Machine Learning. CoRR Bryn Noel Ubald, Pranay Seshadri, Andrew B. Duncan
2022	J	`jnl`	Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. SIAM J. Appl. Dyn. Syst. Oliver R. A. Dunbar, Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
2022	J	`jnl`	F-EBM: Energy Based Learning of Functional Data. CoRR Jen Ning Lim, Sebastian J. Vollmer, Lorenz Wolf, Andrew B. Duncan
2022	A	`conf`	Grassmann Stein Variational Gradient Descent. AISTATS Xing Liu, Harrison Zhu, Jean-Francois Ton, George Wynne, Andrew B. Duncan
2022	J	`jnl`	Grassmann Stein Variational Gradient Descent. CoRR Xing Liu, Harrison Zhu, Jean-François Ton, George Wynne, Andrew B. Duncan
2022	J	`jnl`	Knowledge Transfer in Engineering Fleets: Hierarchical Bayesian Modelling for Multi-Task Learning. CoRR Lawrence A. Bull, Maharshi Harshadbhai Dhada, Olof Steinert, Tony Lindgren, Ajith Kumar Parlikad, Andrew B. Duncan, Mark Girolami
2022	J	`jnl`	Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations. CoRR Chun Yui Wong, Pranay Seshadri, Andrew B. Duncan, Ashley Scillitoe, Geoffrey T. Parks
2021	J	`jnl`	Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods. CoRR Andrew B. Duncan, Andrew M. Stuart, Marie-Therese Wolfram
2021	J	`jnl`	Probabilistic Gradients for Fast Calibration of Differential Equation Models. SIAM/ASA J. Uncertain. Quantification Jonathan Cockayne, Andrew B. Duncan
2021	J	`jnl`	Theoretical Guarantees for the Statistical Finite Element Method. CoRR Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan
2020	J	`jnl`	Bayesian Assessments of Aeroengine Performance. CoRR Pranay Seshadri, Andrew B. Duncan, George Thorne, Geoffrey T. Parks, Mark Girolami
2020	J	`jnl`	Blade Envelopes Part I: Concept and Methodology. CoRR Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Andrew B. Duncan, Geoffrey T. Parks
2020	J	`jnl`	Blade Envelopes Part II: Multiple Objectives and Inverse Design. CoRR Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Bryn Noel Ubald, Andrew B. Duncan, Geoffrey T. Parks
2020	J	`jnl`	Probabilistic Gradients for Fast Calibration of Differential Equation Models. CoRR Jon Cockayne, Andrew B. Duncan
2020	—	`conf`	Scalable Control Variates for Monte Carlo Methods Via Stochastic Optimization. MCQMC Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
2020	J	`jnl`	Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization. CoRR Shijing Si, Chris J. Oates, Andrew B. Duncan, Lawrence Carin, François-Xavier Briol
2019	A*	`conf`	Minimum Stein Discrepancy Estimators. NeurIPS Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
2019	J	`jnl`	Minimum Stein Discrepancy Estimators. CoRR Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey
2019	J	`jnl`	On the geometry of Stein variational gradient descent. CoRR Andrew B. Duncan, Nikolas Nüsken, Lukasz Szpruch
2019	J	`jnl`	Statistical Inference for Generative Models with Maximum Mean Discrepancy. CoRR François-Xavier Briol, Alessandro Barp, Andrew B. Duncan, Mark A. Girolami
2016	J	`jnl`	Hybrid framework for the simulation of stochastic chemical kinetics. J. Comput. Phys. Andrew B. Duncan, Radek Erban, Konstantinos C. Zygalakis
2016	J	`jnl`	Measuring Sample Quality with Diffusions. CoRR Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey
2015	J	`jnl`	A Multiscale Analysis of Diffusions on Rapidly Varying Surfaces. J. Nonlinear Sci. Andrew B. Duncan, Charles M. Elliott, Grigorios A. Pavliotis, Andrew M. Stuart
2015	J	`jnl`	Homogenization of Lateral Diffusion on a Random Surface. Multiscale Model. Simul. Andrew B. Duncan