The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning techniques, next-generation …

Estimating counterfactual outcomes over time from observational data is relevant for many
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …

Gaussian processes are the gold standard for many real-world modeling problems,
especially in cases where a model's success hinges upon its ability to faithfully represent …

Excellent variational approximations to Gaussian process posteriors have been developed
which avoid the $\mathcal {O}\left (N^ 3\right) $ scaling with dataset size $ N $. They reduce …

We develop an efficient and provably no-regret Bayesian optimization (BO) algorithm for
optimization of black-box functions in high dimensions. We assume a generalized additive …

Gaussian process regression is a popular Bayesian framework for surrogate modeling of
expensive data sources. As part of a broader effort in scientific machine learning, many …

Gaussian processes are a versatile framework for learning unknown functions in a manner
that permits one to utilize prior information about their properties. Although many different …

Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …

Gaussian processes are an effective model class for learning unknown functions,
particularly in settings where accurately representing predictive uncertainty is of key …