Publications

2020

• N. J. Higham and T. Mary. Sharper Probabilistic Backward Error Analysis for Basic Linear Algebra Kernels With Random Data, MIMS EPrint 2020.4, 2020.
• 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.

2019

• 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.

2018

• 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.

2017

• 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.