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Chris J. Oates
Person information
- affiliation: University of Technology Sydney, ACEMS
- affiliation: University of Warwick, Department of Statistics
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2020 – today
- 2024
- [i26]Chris J. Oates, Toni Karvonen
, Aretha L. Teckentrup, Marina Strocchi, Steven A. Niederer:
Probabilistic Richardson Extrapolation. CoRR abs/2401.07562 (2024) - [i25]Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates:
Reinforcement Learning for Adaptive MCMC. CoRR abs/2405.13574 (2024) - [i24]Zheyang Shen, Chris J. Oates:
Operator-informed score matching for Markov diffusion models. CoRR abs/2406.09084 (2024) - [i23]Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock:
Scalable Monte Carlo for Bayesian Learning. CoRR abs/2407.12751 (2024) - 2023
- [j25]Toni Karvonen, Chris J. Oates:
Maximum likelihood estimation in Gaussian process regression is ill-posed. J. Mach. Learn. Res. 24: 120:1-120:47 (2023) - [j24]Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne
Statistical properties of BayesCG under the Krylov prior. Numerische Mathematik 155(3-4): 239-288 (2023) - [j23]Marina Strocchi
Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators. PLoS Comput. Biol. 19(6) (2023) - [j22]Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami:
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. Trans. Mach. Learn. Res. 2023 (2023) - [c16]Matthew Fisher, Chris J. Oates:
Gradient-Free Kernel Stein Discrepancy. NeurIPS 2023 - [c15]Congye Wang, Ye Chen, Heishiro Kanagawa, Chris J. Oates:
Stein Π-Importance Sampling. NeurIPS 2023 - [c14]Zhuo Sun, Chris J. Oates, François-Xavier Briol:
Meta-learning Control Variates: Variance Reduction with Limited Data. UAI 2023: 2047-2057 - [i22]Zhuo Sun, Chris J. Oates, François-Xavier Briol:
Meta-learning Control Variates: Variance Reduction with Limited Data. CoRR abs/2303.04756 (2023) - 2022
- [j21]Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur:
Testing Whether a Learning Procedure is Calibrated. J. Mach. Learn. Res. 23: 203:1-203:36 (2022) - [j20]Chris J. Oates, Wilfrid S. Kendall
A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel. Technometrics 64(3): 370-383 (2022) - [i21]Toni Karvonen
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed. CoRR abs/2203.09179 (2022) - [i20]Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates:
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. CoRR abs/2208.03885 (2022) - [i19]Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami:
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres. CoRR abs/2211.09196 (2022) - 2021
- [j19]Takuo Matsubara, Chris J. Oates, François-Xavier Briol:
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. J. Mach. Learn. Res. 22: 157:1-157:57 (2021) - [j18]Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid
Probabilistic Iterative Methods for Linear Systems. J. Mach. Learn. Res. 22: 232:1-232:34 (2021) - [j17]Toni Karvonen
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. Math. Comput. 90(331): 2209-2233 (2021) - [j16]Junyang Wang, Jon Cockayne
Bayesian numerical methods for nonlinear partial differential equations. Stat. Comput. 31(5): 55 (2021) - [j15]Jakub Prüher
Improved Calibration of Numerical Integration Error in Sigma-Point Filters. IEEE Trans. Autom. Control. 66(3): 1286-1292 (2021) - [c13]Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates:
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. AISTATS 2021: 1027-1035 - [c12]Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates:
Measure Transport with Kernel Stein Discrepancy. AISTATS 2021: 1054-1062 - [c11]Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates:
Black Box Probabilistic Numerics. NeurIPS 2021: 23452-23464 - [i18]Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates:
Bayesian Numerical Methods for Nonlinear Partial Differential Equations. CoRR abs/2104.12587 (2021) - [i17]Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris J. Oates:
Black Box Probabilistic Numerics. CoRR abs/2106.13718 (2021) - 2020
- [j14]Toni Karvonen
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. SIAM/ASA J. Uncertain. Quantification 8(3): 926-958 (2020) - [c10]Matthew Fisher, Chris J. Oates, Catherine E. Powell, Aretha L. Teckentrup:
A Locally Adaptive Bayesian Cubature Method. AISTATS 2020: 1265-1275 - [c9]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 - [i16]Toni Karvonen, George Wynne, Filip Tronarp, Chris J. Oates, Simo Särkkä:
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions. CoRR abs/2001.10965 (2020) - [i15]Toni Karvonen, Chris J. Oates, Mark Girolami:
Integration in reproducing kernel Hilbert spaces of Gaussian kernels. CoRR abs/2004.12654 (2020) - [i14]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) - [i13]Tim W. Reid
A Probabilistic Numerical Extension of the Conjugate Gradient Method. CoRR abs/2008.03225 (2020) - [i12]Onur Teymur, Jackson Gorham, Marina Riabiz, Chris J. Oates:
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy. CoRR abs/2010.07064 (2020) - [i11]Takuo Matsubara, Chris J. Oates, François-Xavier Briol:
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks. CoRR abs/2010.08488 (2020) - [i10]Jon Cockayne
Probabilistic Iterative Methods for Linear Systems. CoRR abs/2012.12615 (2020)
2010 – 2019
- 2019
- [j13]Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee:
Causal Learning via Manifold Regularization. J. Mach. Learn. Res. 20: 127:1-127:32 (2019) - [j12]Mark A. Girolami
Editorial: special edition on probabilistic numerics. Stat. Comput. 29(6): 1181-1183 (2019) - [j11]Martin Ehler
Optimal Monte Carlo integration on closed manifolds. Stat. Comput. 29(6): 1203-1214 (2019) - [j10]Toni Karvonen
Symmetry exploits for Bayesian cubature methods. Stat. Comput. 29(6): 1231-1248 (2019) - [j9]Chris J. Oates, Timothy John Sullivan:
A modern retrospective on probabilistic numerics. Stat. Comput. 29(6): 1335-1351 (2019) - [j8]Jon Cockayne
Bayesian Probabilistic Numerical Methods. SIAM Rev. 61(4): 756-789 (2019) - [c8]Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates:
Stein Point Markov Chain Monte Carlo. ICML 2019: 1011-1021 - 2018
- [c7]Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. ICML 2018: 843-852 - [c6]Toni Karvonen, Chris J. Oates, Simo Särkkä:
A Bayes-Sard Cubature Method. NeurIPS 2018: 5886-5897 - [i9]Jon Cockayne, Chris J. Oates, Mark A. Girolami:
A Bayesian Conjugate Gradient Method. CoRR abs/1801.05242 (2018) - [i8]Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. CoRR abs/1803.10161 (2018) - [i7]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR abs/1811.10275 (2018) - [i6]Jakub Prüher, Toni Karvonen, Chris J. Oates, Ondrej Straka, Simo Särkkä:
Improved Calibration of Numerical Integration Error in Sigma-Point Filters. CoRR abs/1811.11474 (2018) - 2017
- [j7]Nial Friel
Investigation of the widely applicable Bayesian information criterion. Stat. Comput. 27(3): 833-844 (2017) - [c5]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. ICML 2017: 586-595 - [c4]Chris J. Oates, Steven A. Niederer, Angela W. C. Lee, François-Xavier Briol, Mark A. Girolami:
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models. NIPS 2017: 110-118 - [i5]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. CoRR abs/1701.04006 (2017) - [i4]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Bayesian Probabilistic Numerical Methods. CoRR abs/1702.03673 (2017) - [i3]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. CoRR abs/1706.03369 (2017) - 2016
- [j6]Chris J. Oates, Jim Q. Smith, Sach Mukherjee:
Estimating Causal Structure Using Conditional DAG Models. J. Mach. Learn. Res. 17: 54:1-54:23 (2016) - [j5]Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
Exact estimation of multiple directed acyclic graphs. Stat. Comput. 26(4): 797-811 (2016) - [c3]Chris J. Oates, Mark A. Girolami:
Control Functionals for Quasi-Monte Carlo Integration. AISTATS 2016: 56-65 - [i2]Jon Cockayne
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. CoRR abs/1605.07811 (2016) - 2015
- [j4]Chris J. Oates, Lilia Carneiro da Costa, Tom E. Nichols
Toward a Multisubject Analysis of Neural Connectivity. Neural Comput. 27(1): 151-170 (2015) - [c2]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne:
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. NIPS 2015: 1162-1170 - [i1]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Probabilistic Integration. CoRR abs/1512.00933 (2015) - 2014
- [j3]Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee:
Causal network inference using biochemical kinetics. Bioinform. 30(17): 468-474 (2014) - [c1]Chris J. Oates, Sach Mukherjee:
Joint Structure Learning of Multiple Non-Exchangeable Networks. AISTATS 2014: 687-695 - 2013
- [j2]Chris J. Oates, Bryan T. J. Hennessy
Network inference using steady-state data and Goldbeter-Koshland kinetics. Bioinform. 29(6): 819 (2013) - 2012
- [j1]Chris J. Oates, Bryan T. J. Hennessy
Network inference using steady-state data and Goldbeter-koshland kinetics. Bioinform. 28(18): 2342-2348 (2012)
Coauthor Index
aka: Mark Girolami
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last updated on 2024-09-13 00:43 CEST by the dblp team
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