9 papers
Exponential Smoothing for Time Series of Random Objects
Takuo Matsubara, Peiwen Jiang, Wilson Ye Chen +1
Time series of random objects, such as covariance matrices, probability distributions, and functional data, call for forecasting methods that do not rely on standard arithmetic ope…
Generalised Robust Bayes for Joint Inference of Model and Contamination
Masahiro Fujisawa, Masaki Adachi, Takuo Matsubara
Robust generalised Bayesian methods protect parameter inference from contamination but do not generally infer its proportion or identify individual outliers. We propose Hölder--Bay…
Wasserstein Exponential Smoothing for Distributional Time Series Forecasting
Takuo Matsubara, Peiwen Jiang, Minh-Ngoc Tran +1
Distributional time series arise when each temporal observation is a probability distribution rather than a scalar. We propose Wasserstein exponential smoothing (WES), a one-parame…
Inversion-Free Natural Gradient Descent on Riemannian Manifolds
Dario Draca, Takuo Matsubara, Minh-Ngoc Tran
The natural gradient method is a central tool for statistical optimisation, but its broader application is hindered by the assumption of a Euclidean parameter space, the repeated e…
Maximin Robust Bayesian Experimental Design
Hany Abdulsamad, Sahel Iqbal, Christian A. Naesseth +2
We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between the experimenter and an adversarial na…
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
Peiwen Jiang, Takuo Matsubara, Minh-Ngoc Tran
The Importance-Weighted Evidence Lower Bound (IW-ELBO) has emerged as an effective objective for variational inference (VI), tightening the standard ELBO and mitigating the mode-se…
Sampling as Bandits: Evaluation-Efficient Design for Black-Box Densities
Takuo Matsubara, Andrew Duncan, Simon Cotter +1
We propose bandit importance sampling (BIS), a powerful importance sampling framework tailored for settings in which evaluating the target density is computationally expensive. BIS…
Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
Takuo Matsubara
Gradient boosting is a sequential ensemble method that fits a new weaker learner to pseudo residuals at each iteration. We propose Wasserstein gradient boosting, a novel extension…
Hamiltonian Dynamics of Bayesian Inference Formalised by Arc Hamiltonian Systems
Takuo Matsubara
This paper advances theoretical understanding of infinite-dimensional geometrical properties associated with Bayesian inference. First, we introduce a novel class of infinite-dimen…