From the 1 of 6 linked papers with an AI index.
7 papers
Generalised Robust Bayes for Joint Inference of Model and Contamination
Masahiro Fujisawa, Masaki Adachi, Takuo Matsubara
The paper proposes Hӧlder-Bayes, a generalized Bayesian framework that jointly infers model parameters and the proportion of contaminated data, providing built‑in outlier detection…
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…
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…
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…