In consumer theory, ranking available objects by means of preference
rel...
Contemporary undertakings provide limitless opportunities for widespread...
We introduce a formulation of quantum theory (QT) as a general probabili...
Gaussian processes (GPs) are an important tool in machine learning and
s...
In this work we introduce a new framework for multi-objective Bayesian
o...
We present a two-stage Metropolis-Hastings algorithm for sampling
probab...
Bayesian optimization (BO) is an approach to globally optimizing black-b...
A fundamental task in AI is to assess (in)dependence between mixed-type
...
Skew-Gaussian processes (SkewGPs) extend the multivariate Unified Skew-N...
Automatic forecasting is the task of receiving a time series and returni...
Bayesian optimisation (BO) is a very effective approach for sequential
b...
Sparse inducing points have long been a standard method to fit Gaussian
...
Gaussian processes (GPs) are distributions over functions, which provide...
Gaussian Processes (GPs) are powerful kernelized methods for non-paramet...
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
Quantum theory (QT) has been confirmed by numerous experiments, yet we s...
Usually one compares the accuracy of two competing classifiers via null
...
The machine learning community adopted the use of null hypothesis
signif...
The state space (SS) representation of Gaussian processes (GP) has recen...
The statistical comparison of multiple algorithms over multiple data set...
Credal networks are graph-based statistical models whose parameters take...