6 days ago · Gaussian processes (GPs) are a class of stochastic processes that are widely used as non- parametric machine learning models (Rasmussen, 2003).

3 days ago · This model can be a Gaussian Process (GP) [36], which predicts the performance of different hyperparameter settings based on past evaluations.

1 day ago · 3 Gaussian Process Optimistic Optimization​​ The policy of OO is based on measuring (dis)similarity in the input domain in terms of a pseudo-metric. It turns out ...

3 days ago · To address the above challenges, this study proposes a Gaussian process regression model based on stochastic data segmentation. It aims to optimize the ...

7 days ago · Therefore, we propose a novel method for transfer learning optimization that operates within a local space (LSTL-PBO). This method employs partial data acquired ...

5 days ago · Monte Carlo simulation of a case study using Gaussian process regression is conducted for verification and comparison with the Meta-prior algorithm. I.

4 days ago · We present a novel statistical framework, based on hierarchical Gaussian process models and named GPMelt, to make TPP-TR datasets analysis unbiased with ...

7 days ago · This paper explores a federated learning approach that automatically selects the number of latent processes in multi-output Gaussian processes (MGPs). The MGP ...

3 days ago · Furthermore, Gaussian process regression can simultaneously consider factor information and stock price volatility information, and provide uncertainty interval ...

5 days ago · We propose a method for creating a meaningful off- manifold geometry with a non-Euclidean metric on the data space that penalizes movement off the manifold. 3.