Past Presentations
Past Presentations
Matsubara, T. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Seminar, RIKEN AIP Seminar Series (2022).
Matsubara, T. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Contributed Talk, 2022 Joint Statistical Meeting, Online, (2022).
Matsubara, T. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Contributed Talk, The 5th International Conference on Econometrics and Statistics, Online, (2022).
Matsubara, T., Knoblauch, J., Briol, F-X., Oates, C. J. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Poster Presentation, NeurIPS 2021 Workshop Your Model is Wrong, Online, (2021).
Matsubara, T. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Contributed Talk, End-to-end Bayesian learning International Conference, CIRM, (2021).
Matsubara, T. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Oral Presentation, The 13th International Conference on Monte Carlo Methods and Applications, Online, (2021).
Matsubara, T. "Robust Generalised Bayesian Inference for Intractable Likelihoods", Contributed Talk, 2021 World Meeting of the International Society for Bayesian Analysis, Online, (2021). The Best Student/Postdoc Paper Award.
Matsubara, T. "Approximation of Gaussian Process by Bayesian Neural Network", Oral Presentation, The 4th International Conference on Econometrics and Statistics, Online, (2021).
Matsubara, T., Oates, C. J., Briol, F-X. "The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks.", Poster Presentation, NeurIPS Europe meetup on Bayesian Deep Learning, (2020).
Matsubara, T. and Oates. C. "The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks", Online Talk, Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale, (2020).
Matsubara, T. “Quadrature of Bayesian neural networks”, Oral presentation, The 14th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing Minisymposium of Probabilistic Numerical and Kernel- Based Methods, Oxford, United Kingdom (2020).
Matsubara, T. "Approximation of Gaussian Process by Bayesian Neural Network", Poster Presentation, Workshop on Functional Inference and Machine Intelligence 2020, EURECOM, France (2020).
Matsubara, T. “Quadrature of neural networks based on the ridgelet transform, and the possibility of the extension”, Oral presentation, Deep Structures 2019 Joint Workshop by the Alan Turing Institute and the Finnish Center for Artificial Intelligence, Espoo, Finland (2019).
Matsubara, T. “Bayesian quadrature of neural networks based on the ridgelet transform”, Oral presentation, Probabilistic Numerics Workshop 2019, London, United Kingdom (2019).
Sonoda, S., Ishikawa, I., Ikeda, M., Hagihara, K., Sawano, Y., Matsubara, T., Murata, N. “An explicit expression for the global minimizer network”, Poster presentation, The 35th International Conference on Machine Learning Workshop on Deep Learning Theory, Stockholm, Sweden (2018).