no code implementations • ICML 2020 • Futoshi Futami, Issei Sato, Masashi Sugiyama
Compared with the naive parallel-chain SGLD that updates multiple particles independently, ensemble methods update particles with their interactions.
no code implementations • 23 Jul 2023 • Futoshi Futami, Tomoharu Iwata
Furthermore, we extend the existing analysis of Bayesian meta-learning and show the novel sensitivities among tasks for the first time.
no code implementations • 2 Jun 2022 • Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
Bayesian deep learning plays an important role especially for its ability evaluating epistemic uncertainty (EU).
no code implementations • NeurIPS 2021 • Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Issei Sato, Masashi Sugiyama
First, we provide a new second-order Jensen inequality, which has the repulsion term based on the loss function.
no code implementations • 10 Mar 2020 • Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami, Issei Sato, Junya Honda, Masashi Sugiyama
If the black-box function varies with time, then time-varying Bayesian optimization is a promising framework.
no code implementations • 21 May 2018 • Futoshi Futami, Zhenghang Cui, Issei Sato, Masashi Sugiyama
Another example is the Stein points (SP) method, which minimizes kernelized Stein discrepancy directly.
1 code implementation • 18 Oct 2017 • Futoshi Futami, Issei Sato, Masashi Sugiyama
In this paper, based on Zellner's optimization and variational formulation of Bayesian inference, we propose an outlier-robust pseudo-Bayesian variational method by replacing the Kullback-Leibler divergence used for data fitting to a robust divergence such as the beta- and gamma-divergences.
no code implementations • NeurIPS 2017 • Futoshi Futami, Issei Sato, Masashi Sugiyama
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters.