Publications

S. Virtanen and M. Girolami. Dynamic Content Based Ranking. To appear in AISTATS 2020.
A. Barp, F-X. Briol., A. B. Duncan, M. Girolami and L. Mackey. Minimum Stein discrepancy estimators, arXiv:1906.08283. To appear in Neural Information Processing Systems.
S. Virtanen and M. Girolami. Precision-Recall Balanced Topic Modelling. To appear in NeurIPS 2019.
(pdf), (code), (poster)
N. J. Higham and T. Mary. A New Approach to Probabilistic Rounding Error Analysis, SIAM J. Sci. Comput., 41(5):A2815-A2835, 2019.
N. J. Higham and T. Mary. Solving Block Low-Rank Linear Systems by LU Factorization is Numerically Stable, MIMS EPrint 2019.15, 2019.
P. Blanchard, D. J. Higham and N. J. Higham. Accurate Computation of the Log-Sum-Exp and Softmax Functions, MIMS EPrint 2019.16, 2019.
P. Blanchard, N. J. Higham and T. Mary. A Class of Fast and Accurate Summation Algorithms, MIMS EPrint 2019.6, 2019.
O. Hamelijnck, T. Damoulas, K. Wang, M. Girolami, Multi-resolution Multi-task Gaussian Processes. Thirty-third Conference on Neural Information Processing Systems, NeurIPS 2019, arXiv preprint arXiv:1906.08344, 2019.
W. Y. Chen, A. Barp, F- X. Briol, J. Gorham, M. Girolami, L. Mackey, C. J. Oates. Stein point Markov chain Monte Carlo, International Conference on Machine Learning, PMLR 97:1011-1021, 2019.
F-X. Briol, C. J. Oates, M. Girolami, M. A. Osborne and D. Sejdinovic. Probabilistic integration: a role in statistical computation? Statistical Science, Vol 34, Number 1, pp1-22, 2019.
C. J. Oates, J. Cockayne, F-X. Briol and M. Girolami, Convergence rates for a class of estimators based on Stein’s identity, Bernoulli, Vol. 25, No. 2, 1141-1159.
N. J. Higham and S. Pranesh. Simulating Low Precision Floating-Point Arithmetic, MIMS EPrint 2019.4, 2019.
J. Cockayne, C. J. Oates, I. Ipsen and M. Girolami. A Bayesian Conjugate Gradient Method, Bayesian Anal., advance publication, 18 May 2019. doi:10.1214/19-BA1145, 2019.
H. Anzt, J. Dongarra, G. Flegar, N. J. Higham and E. S. Quintana-Orti. Adaptive Precision in Block-Jacobi Preconditioning for Iterative Sparse Linear System Solvers, Concurrency Computat.: Pract. Exper, 31(6), e4460, 2019.
N. J. Higham and T. Mary. A new preconditioner that exploits low-rank approximations to factorization error, SIAM J. Sci. Comput., 41(1), A59-A82, 2019.
M. Fasi and N. J. Higham. An Arbitrary Precision Scaling and Squaring Algorithm for the Matrix Exponential, MIMS EPrint 2018.36, 2018.
A.Haidar, S. Tomov, J. Dongarra and N. J. Higham. Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers, In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (p. 47), IEEE Press, 2018.
M. M. Dunlop, M. A. Girolami, A. M. Stuart and A. L. Teckentrup. How deep are deep Gaussian processes?, The Journal of Machine Learning Research, 19(1), 2100-2145, 2018.
M. Fasi and N. J. Higham. Multiprecision Algorithms for Computing The Matrix Logarithm, SIAM J. Matrix Anal. Appl., 39(1):472-491, 2018.
L. Ellam, G. Pavliotis, M. Girolami, A. Wilson, Stochastic modelling of urban structure, Proc. R. Soc. A, rspa.2017.0700, 2018.
M. Croci, M.B. Giles, M.E. Rognes, P.E. Farrell. Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes, SIAM/ASA Journal on Uncertainty Quantification, 6(4):1630-1655, 2019.
Fang, M.B. Giles. Multilevel Monte Carlo method for ergodic SDEs without contractivity, Journal of Mathematical Analysis and Applications, 476(1):149-176, 2019.
M. B. Giles, Multilevel Monte Carlo methods, Acta Numerica, 10.1017/S09624929, 2018.
M. Fasi and N. J. Higham. Multiprecision Algorithms for Computing the Matrix Logarithm, SIAM J. Matrix Anal. Appl., 39(1), 472–491, 2018.
E. Carson and N, J. Higham, Accelerating the Solution of Linear Systems by Iterative Refinement in Three Precisions, SIAM J. Sci. Comput., 40(2), A817–A847, 2018.
H. Anzt, J. Dongarra, G. Flegar, N. J. Higham and E. S. Quintana-Orti, Adaptive Precision in Block-Jacobi Preconditioning for Iterative Sparse Linear System Solvers, Concurrency Computat.: Pract. Exper., 2018.
C. F. Higham and D. J. Higham. Deep Learning: An Introduction for Applied Mathematicians, arXiv:1801.05894, 2018.
A. Barp, F-X. Briol, A. D. Kennedy and M. Girolami. Geometry and dynamics for Markov chain Monte Carlo, Annual Review of Statistics and Its Applications, Vol. 5:451-471, 2018.
F-X. Briol, C. J. Oates, J. Cockayne, W. Y. Chen and M. Girolami. On the sampling problem for kernel quadrature, Proceedings of the 34th International Conference on Machine Learning, PMLR 70:586-595, 2017.
E. Carson and N. J. Higham. A new analysis of iterative refinement and its application to accurate solution of ill-conditioned sparse linear systems, SIAM J. Sci. Comput., 39(6):A2834-A2856, 2017.
L. Ellam, H. Strathmann, M. Girolami and I. Murray, A determinant-free method to simulate the parameters of large Gaussian fields, Stat, 6(1), 271-281, 2017.
Fang, M.B. Giles. Adaptive Euler-Maruyama method for SDEs with non-globally Lipschitz drift, Annals of Applied Probability, to appear 2019.

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