Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Matsubara, Takuo"'
Autor:
Matsubara, Takuo
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 of gradient boosting that fits a new weak learner to alternative pseud
Externí odkaz:
http://arxiv.org/abs/2405.09536
Autor:
Matsubara, Takuo
This paper advances theoretical understanding of infinite-dimensional geometrical properties associated with Bayesian inference. First, we introduce a novel class of infinite-dimensional Hamiltonian systems for saddle Hamiltonian functions whose doma
Externí odkaz:
http://arxiv.org/abs/2310.07680
This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE). TCE incorporates a novel loss function based on a statistical test to examine the extent to which model
Externí odkaz:
http://arxiv.org/abs/2306.14343
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical. This paper addresses this co
Externí odkaz:
http://arxiv.org/abs/2206.08420
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible mis-specification of the likelihood. Here we consider generalised Bayesian inference
Externí odkaz:
http://arxiv.org/abs/2104.07359
Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated with the predictive output in the Bayesian framework. However, it remains unclear how to endow the p
Externí odkaz:
http://arxiv.org/abs/2010.08488
Autor:
Sonoda, Sho, Ishikawa, Isao, Ikeda, Masahiro, Hagihara, Kei, Sawano, Yoshihiro, Matsubara, Takuo, Murata, Noboru
We prove that the global minimum of the backpropagation (BP) training problem of neural networks with an arbitrary nonlinear activation is given by the ridgelet transform. A series of computational experiments show that there exists an interesting si
Externí odkaz:
http://arxiv.org/abs/1805.07517
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Autor:
Matsubara, Takuo1,2 (AUTHOR) tmatsubara@turing.ac.uk, Knoblauch, Jeremias2,3 (AUTHOR), Briol, François‐Xavier2,3 (AUTHOR), Oates, Chris J.1,2 (AUTHOR)
Publikováno v:
Journal of the Royal Statistical Society: Series B (Statistical Methodology). Jul2022, Vol. 84 Issue 3, p997-1022. 26p.
Akademický článek
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