Sampling is ubiquitous in machine learning methodologies. Due to the growth of large
datasets and model complexity, we want to learn and adapt the sampling process while …

The marginal maximum a posteriori probability (MAP) estimation problem, which calculates
the mode of the marginal posterior distribution of a subset of variables with the remaining …

We present a novel approach for multilabel classification based on an ensemble of
Bayesian networks. The class variables are connected by a tree; each model of the …

Credal networks extend Bayesian networks to allow for imprecision in probability values.
Marginal MAP is a widely applicable mixed inference task that identifies the most likely …

Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic
decision-making and statistical reasoning. Its general formulation captures many real-world …

This work presents novel algorithms for learning Bayesian networks of bounded treewidth.
Both exact and approximate methods are developed. The exact method combines mixed …

Marginal MAP inference involves making MAP predictions in systems defined with latent
variables or missing information. It is significantly more difficult than pure marginalization …

Arising from many applications at the intersection of decision-making and machine learning,
Marginal Maximum A Posteriori (Marginal MAP) problems unify the two main classes of …

Learning Bayesian networks with bounded tree-width has attracted much attention recently,
because low tree-width allows exact inference to be performed efficiently. Some existing …

This paper explores the anytime performance of search-based algorithms for solving the
Marginal MAP task over graphical models. The current state of the art for solving this …