We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumptions of a large class of co-training type algorithms, and also clarifies the circumstances under which these assumptions fail. Building upon new insights from this model, we propose an improved method for co-training, whi...

The traditional Monte Carlo sampling of waves requires generating tens thousands random to achieve stable statistics in the tail distribution, which is computationally costly for numerical simulations and time-consuming often impractical experiments. To improve efficiency, we present a sequential method, predicts wave based on nonlinear response deterministic groups. This data-driven method sta...

An increasing number of time-consuming simulators exhibit a complex noise structure that depends on the inputs. For conducting studies with limited budgets evaluations, new surrogate methods are required in order to simultaneously model mean and variance fields. To this end, we present hetGP package, implementing many recent advances Gaussian process modeling input-dependent noise. First, descr...

Bayesian Optimization (BO) aims at optimizing an unknown function that is costly to evaluate. We focus on applications where concurrent function evaluations are possible. In such cases, BO could choose to either sequentially evaluate the function (sequential mode) or evaluate the function at a batch of multiple inputs at once (batch mode). The sequential mode generally leads to better optimizat...