While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, potential benefits of utilizing correlations between frames received less attention. Even though probabilistic machine learning provides ability encode correlation as prior knowledge for inference, there is a tangible gap theory and practice applying methods modern pr...

We investigate a machine learning approach to option Greeks approximation based on Gaussian Process (GP) surrogates. Our motivation is implement Delta hedging in cases where direct computation expensive, such as local volatility models, or can only ever be done approximately. The proposed method takes noisily observed prices, fits non-parametric input-output map and then analytically differenti...

Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning for their nonstationary flexibility and ability to cope with abrupt regime changes training data. Here, we explore DGPs surrogates computer simulation experiments whose response surfaces exhibit similar characteristics. In particular, transport a DGP’s automatic warping of the input space full uncer...

Bayesian nonparametric regression under a rescaled Gaussian process prior offers smoothness-adaptive function estimation with near minimax-optimal error rates. Hierarchical extensions of this approach, equipped stochastic variable selection, are known to also adapt the unknown intrinsic dimension sparse true function. But it remains unclear if such offer selection consistency, that is, subset i...