One of the pillars of machine learning is that of non-linear regression on tabular data. For the
last few decades, the performance of ensemble methods based on a sum-of-trees model …

Overlapping Communities on Large-Scale Networks: Benchmark Generation and Learning via
Adaptive Stochastic Optimization Alessand Page 1 Overlapping Communities on Large-Scale …

Abstract Estimating a joint Highest Posterior Density credible set for a multivariate posterior
density is challenging as dimension gets larger. Credible intervals for univariate marginals …

Bayesian binary probit regression and its extensions to time-dependent observations and
multi-class responses are popular tools in binary and categorical data regression due to …

Unit-level models are an alternative to the traditional area-level models used in small area
estimation, characterized by the direct modeling of survey responses rather than aggregated …

Spatial count data models are used to explain and predict the frequency of phenomena such
as traffic accidents in geographically distinct entities such as census tracts or road segments …

State-of-the-art methods for Bayesian inference on regression models with binary responses
are either computationally impractical or inaccurate in high dimensions. To cover this gap …

Complex dependence structures characterising modern data are routinely encountered in a
large variety of research fields. Medicine, biology, psychology and social sciences are …

The investigation of flexible classes of discrete prior has been an active research line in
Bayesian statistics. Several contributions were devoted to the study of nonparametric priors …