| Robust generalised Bayesian inference for intractable likelihoods T Matsubara, J Knoblauch, FX Briol, CJ Oates Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 64 | 2022 |
| The ridgelet prior: A covariance function approach to prior specification for bayesian neural networks T Matsubara, CJ Oates, FX Briol Journal of Machine Learning Research 22 (157), 1-57, 2021 | 19 | 2021 |
| Generalised Bayesian inference for discrete intractable likelihood T Matsubara, J Knoblauch, FX Briol, C Oates Journal of the American Statistical Association, 1-11, 2023 | 12 | 2023 |
| The global optimum of shallow neural network is attained by ridgelet transform S Sonoda, I Ishikawa, M Ikeda, K Hagihara, Y Sawano, T Matsubara, ... The Thirty-Fifth International Conference on Machine Learning Workshop on …, 2018 | 7 | 2018 |
| TCE: A Test-Based Approach to Measuring Calibration Error T Matsubara, N Tax, R Mudd, I Guy Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial …, 2023 | 2 | 2023 |
| New Learning Algorithm of Neural Network Using Integral Representation and Kernel Herding T Matsubara, S Sonoda, N Murata IEICE Technical Report; IEICE Tech. Rep. 116 (500), 25-31, 2017 | 1 | 2017 |
| Wasserstein Gradient Boosting: A General Framework with Applications to Posterior Regression T Matsubara arXiv preprint arXiv:2405.09536, 2024 | | 2024 |
| Hamiltonian Dynamics of Bayesian Inference Formalised by Arc Hamiltonian Systems T Matsubara arXiv preprint arXiv:2310.07680, 2023 | | 2023 |
| Bridging the Gap Between Modelling and Computation in Bayesian Statistics T Matsubara Newcastle University, 2023 | | 2023 |
François-Xavier BriolAssociate Professor in Statistical Science, UCL在 ucl.ac.uk 的电子邮件经过验证
