4 papers · 1 filter
Generalised Robust Bayes for Joint Inference of Model and Contamination
Masahiro Fujisawa, Masaki Adachi, Takuo Matsubara
Generalised Bayesian inference (GBI) has emerged as a compelling robust alternative to standard Bayesian inference, mitigating sensitivity to data contamination by replacing the lo…
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…
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…