4 accepted papers
Gaussian processes (GPs) offer a flexible, uncertainty-aware framework for modeling complex signals, but scale cubically with data, assume static targets, and are brittle to outliers, limiting their applicability in large-scale problems with dynamic and noisy environments. Recent work introduced dec…
Bayesian Optimization (BO) is a popular black-box optimization method consisting of a surrogate model, typically a probabilistic model such as a Gaussian Process (GP) and an Acquisition function (AF). Effective selection of these functions has a strong impact on the optimization process. Existing en…
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed setting, Bayesian solutions are much more limited. We introduce a fully decentrali…
Bayesian optimization (BO) is a popular approach to optimizing costly, black-box functions that rely on a statistical surrogate model of the function to select new query points, balancing exploration and exploitation of the parameter space. Most of the work on BO has focused on the time-invariant se…