Zobrazeno 1 - 10
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pro vyhledávání: '"Zou, Lu"'
Autor:
Zou, Lu, Ding, Liang
Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the convergence rate of Back-fitting in training additive GPs is still an ope
Externí odkaz:
http://arxiv.org/abs/2403.13300
An asymptotic theory is established for linear functionals of the predictive function given by kernel ridge regression, when the reproducing kernel Hilbert space is equivalent to a Sobolev space. The theory covers a wide variety of linear functionals
Externí odkaz:
http://arxiv.org/abs/2403.04248
Gaussian Process Upper Confidence Bound (GP-UCB) is one of the most popular methods for optimizing black-box functions with noisy observations, due to its simple structure and superior performance. Its empirical successes lead to a natural, yet unres
Externí odkaz:
http://arxiv.org/abs/2312.01386
Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques. In this paper, we treat conditional expectation as a function of the multidimensional conditioning variable and provide asym
Externí odkaz:
http://arxiv.org/abs/2310.11756
Among generalized additive models, additive Mat\'ern Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure and stochastic differential equation representation, back-fitting-bas
Externí odkaz:
http://arxiv.org/abs/2305.00324
Publikováno v:
In Ocean Engineering 15 November 2024 312 Part 2
Publikováno v:
In Ocean Engineering 15 November 2024 312 Part 2
Publikováno v:
In Ocean Engineering 15 November 2024 312 Part 2
Publikováno v:
In Reliability Engineering and System Safety September 2024 249