Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art
solvers for this task are based on a hybrid best-first and depth-first search scheme that …

Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art
algorithms for solving exactly this task are based on either depth-first or best-first sequential …

We introduce new anytime search algorithms that combine best-first with depth-first search
into hybrid schemes for Marginal MAP inference in graphical models. The main goal is to …

Marginal MAP is known to be a difficult task for graphical models, particularly because the
evaluation of each MAP assignment involves a conditional likelihood computation. In order …

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 …

Marginal MAP is a key task in Bayesian inference and decision-making. It is known to be
very difficult in general, particularly because the evaluation of each MAP assignment …

Mixed inference such as the marginal MAP query (some variables marginalized by
summation and others by maximization) is key to many prediction and decision models. It is …

Marginal MAP is a key task in Bayesian inference and decision-making, and known to be
very challenging in general. In this paper, we present an algorithm that blends heuristic …

The Marginal MAP inference task is known to be extremely hard particularly because the
evaluation of each complete MAP assignment involves an exact likelihood computation (a …

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 …