Skew Gaussian Processes: a unified framework for closed-form nonparametric regression, classification, preference and mixed problems. Gaussian Processes (GPs) are powerful nonparametric distributions over functions.
May 26, 2020 · Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification.
Sep 4, 2020 · Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification.
May 5, 2020 · Gaussian processes (GPs) are distributions over functions, which provide a Bayesian non- parametric approach to regression and classification.
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Sep 4, 2020 · Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification.
Dec 12, 2020 · SkewGPs are more general and flexible than Gaussian processes, as SkewGPs may also represent asymmetric distributions. In a recent contribution ...
Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions.
We study preferential Bayesian optimization (BO) where reliable feedback is limited to pairwise comparison called duels. An important challenge.
A unified framework for closed-form nonparametric regression ...
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Sep 13, 2021 · Skew-Gaussian Processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions.
SkewGPs are conju- gate with both the normal and affine probit likelihood and, more in general, with their product. This allows us to derive their posterior for ...