While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Abstract Machine-learning models that learn from data to predict how protein sequence
encodes function are emerging as a useful protein engineering tool. However, when using …

Multivariate time series with missing values are common in areas such as healthcare and
finance, and have grown in number and complexity over the years. This raises the question …

Conventional variational autoencoders fail in modeling correlations between data points
due to their use of factorized priors. Amortized Gaussian process inference through GP …

When fitting Bayesian machine learning models on scarce data, the main challenge is to
obtain suitable prior knowledge and encode it into the model. Recent advances in meta …

Bayesian methods are well suited for learning tasks in the low-data limit, because they offer
a principled way to include prior knowledge about the problem [22]. If the prior knowledge is …

Though recent works have developed methods that can generate estimates (or imputations)
of the missing entries in a dataset to facilitate downstream analysis, most depend on …

We consider a combinatorial Gaussian process semi-bandit problem with time-varying arm
availability. Each round, an agent is provided a set of available base arms and must select a …

Bayesian optimization (BO) is a versatile and robust global optimization method under
uncertainty. However, most of the BO algorithms were developed for problems with only …

Multivariate time series with missing values are common in areas such as healthcare and
finance, and have grown in number and complexity over the years. This raises the question …