Scholar
Takuo Matsubara
Google Scholar ID: htGgB6wAAAAJ
William Gordon Seggie Brown Fellow
Citations & Impact
Citations
190
H-index
5
i10-index
3
Publications
10
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- Publications include 'Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning' (NeurIPS 2024), 'Generalised Bayesian Inference for Discrete Intractable Likelihoods' (Journal of the American Statistical Association, 2023). Awards: ASA SBSS Student Paper Competition Award 2022, NeurIPS 2021 Best Reviewer Awards, etc.
Research Experience
- Independent William Gordon Seggie Brown research fellow, School of Mathematics, The University of Edinburgh; Research Scientist Intern and academic collaborator at Meta.
Education
- PhD, Newcastle University, Supervisor: Prof. Chris Oates; Turing doctoral student at The Alan Turing Institute.
Background
- Research interests: methodologies to estimate and assess predictive uncertainty of machine learning models, computationally efficient Bayesian methodologies for modern complex models, and theoretical foundations of robustness of Bayesian statistics. Worked on projects including Wasserstein gradient boosting and Hamiltonian dynamical structure.
Miscellany
- Organiser: The University of Edinburgh Stats Seminar (2024 - present), DCE Reading Group in The Alan Turing Institute (2020 - 2021).
Co-authors