Timothy John Sullivan

According to our database, Timothy John Sullivan authored at least 35 papers between 2004 and 2024.

Collaborative distances:

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

2024

Autoencoders in Function Space.

Justin Bunker, Mark Girolami, Hefin Lambley, Andrew M. Stuart, Tim Sullivan

CoRR, 2024

2023

An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems.

Hefin Lambley, Timothy John Sullivan

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

Error Bound Analysis of the Stochastic Parareal Algorithm.

Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan

SIAM J. Sci. Comput., October, 2023

GParareal: a time-parallel ODE solver using Gaussian process emulation.

Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan, James Buchanan, Lynton C. Appel

Stat. Comput., 2023

Transporting Higher-Order Quadrature Rules: Quasi-Monte Carlo Points and Sparse Grids for Mixture Distributions.

Ilja Klebanov, Timothy John Sullivan

CoRR, 2023

2022

Testing Whether a Learning Procedure is Calibrated.

Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur

J. Mach. Learn. Res., 2022

2021

Bayesian numerical methods for nonlinear partial differential equations.

Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates

Stat. Comput., 2021

Compression, Inversion, and Approximate PCA of Dense Kernel Matrices at Near-Linear Computational Complexity.

Florian Schäfer, Timothy John Sullivan, Houman Owhadi

Multiscale Model. Simul., 2021

Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432).

Philipp Hennig, Ilse C. F. Ipsen, Maren Mahsereci, Tim Sullivan

Dagstuhl Reports, 2021

Dimension-independent Markov chain Monte Carlo on the sphere.

Han Cheng Lie, Daniel Rudolf, Björn Sprungk, Timothy John Sullivan

CoRR, 2021

Γ-convergence of Onsager-Machlup functionals. Part II: Infinite product measures on Banach spaces.

Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie, Timothy John Sullivan

CoRR, 2021

Γ-convergence of Onsager-Machlup functionals. Part I: With applications to maximum a posteriori estimation in Bayesian inverse problems.

Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie, Timothy John Sullivan

CoRR, 2021

Randomised one-step time integration methods for deterministic operator differential equations.

Han Cheng Lie, Martin Stahn, Timothy John Sullivan

CoRR, 2021

2020

A Rigorous Theory of Conditional Mean Embeddings.

Ilja Klebanov, Ingmar Schuster, Timothy John Sullivan

SIAM J. Math. Data Sci., 2020

Convergence rates of Gaussian ODE filters.

Hans Kersting, Timothy John Sullivan, Philipp Hennig

Stat. Comput., 2020

Geodesic Analysis in Kendall's Shape Space with Epidemiological Applications.

Esfandiar Nava-Yazdani, Hans-Christian Hege, Timothy John Sullivan, Christoph von Tycowicz

J. Math. Imaging Vis., 2020

Optimal Bounds on Nonlinear Partial Differential Equations in Model Certification, Validation, and Experimental Design.

Mike McKerns, Francis J. Alexander, Kyle S. Hickmann, Timothy John Sullivan, Diane E. Vaughan

CoRR, 2020

Adaptive Reconstruction of Imperfectly Observed Monotone Functions, with Applications to Uncertainty Quantification.

Luc Bonnet, Jean-Luc Akian, Éric Savin, Timothy John Sullivan

Algorithms, 2020

2019

Bayesian Probabilistic Numerical Methods.

Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami

SIAM Rev., 2019

A modern retrospective on probabilistic numerics.

Chris J. Oates, Timothy John Sullivan

Stat. Comput., 2019

Strong convergence rates of probabilistic integrators for ordinary differential equations.

Han Cheng Lie, Andrew M. Stuart, Timothy John Sullivan

Stat. Comput., 2019

Editorial: special edition on probabilistic numerics.

Mark A. Girolami, Ilse C. F. Ipsen, Chris J. Oates, Art B. Owen, Timothy John Sullivan

Stat. Comput., 2019

Comments on the article "A Bayesian conjugate gradient method".

Timothy John Sullivan

CoRR, 2019

Error Bounds for Some Approximate Posterior Measures in Bayesian Inference.

Han Cheng Lie, Timothy John Sullivan, Aretha L. Teckentrup

Proceedings of the Numerical Mathematics and Advanced Applications ENUMATH 2019 - European Conference, Egmond aan Zee, The Netherlands, September 30, 2019

2018

Random Forward Models and Log-Likelihoods in Bayesian Inverse Problems.

Han Cheng Lie, Timothy John Sullivan, Aretha L. Teckentrup

SIAM/ASA J. Uncertain. Quantification, 2018

Implicit Probabilistic Integrators for ODEs.

Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems.

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

CoRR, 2017

2016

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems.

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

CoRR, 2016

2015

On the Brittleness of Bayesian Inference.

Houman Owhadi, Clint Scovel, Timothy John Sullivan

SIAM Rev., 2015

2013

Optimal Uncertainty Quantification.

Houman Owhadi, Clint Scovel, Timothy John Sullivan, Mike McKerns, Michael Ortiz

SIAM Rev., 2013

2012

The Optimal Uncertainty Algorithm in the Mystic Framework

Mike McKerns, Houman Owhadi, Clint Scovel, Timothy John Sullivan, Michael Ortiz

CoRR, 2012

Optimal uncertainty quantification for legacy data observations of Lipschitz functions.

Timothy John Sullivan, Mike McKerns, Dominik Meyer, Florian Theil, Houman Owhadi, Michael Ortiz

CoRR, 2012

2005

Application of a regionalized knowledge-based model for classifying the impacts of nitrogen, sulfur, and organic acids on lakewater chemistry.

Timothy John Sullivan, Michael C. Saunders, K. A. Tonnessen, B. L. Nash, B. J. Miller

Knowl. Based Syst., 2005

A knowledge-based approach for classifying lake water chemistry.

Michael C. Saunders, Timothy John Sullivan, B. L. Nash, K. A. Tonnessen, B. J. Miller

Knowl. Based Syst., 2005

2004

Identifying indicators of chemical, biological, radiological, and nuclear (CBRN) weapons development activity in sub-national terrorist groups.

Timothy John Sullivan, W. L. Perry

J. Oper. Res. Soc., 2004