Oceania
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
To address this issue, various approximate Bayesian inference methods have been proposed, such as Markov Chain Monte Carlo [MCMC, see e.g.
A Framework for Efficient Monte-Carlo Bayesian Optimization
"one-shot" formulation of the Knowledge Gradient, enabled by a combination of
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations
Each attribute combination, called an'arm', is associated with a Beta density from which the service's accuracy is sampled. We expect the GP to smooth the parameters of the Beta density over related arms to mitigate sparsity.
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Joao Marques-Silva
In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers. Furthermore, we show that the enumeration of PI-explanations can be obtained with polynomial delay.
Invariant and Transportable Representations for Anti-Causal Domain Shifts
However, many real-world problems are "anti-causal" in the sense that
