We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the minibucket heuristic. We present and analyze methods...

Despite the apparent advantages of and recent advances in the use of visualization in engineering design and optimization, we have found little evidence in the engineering literature that assesses the impact of fast, graphical design interfaces on the efficiency and effectiveness of engineering design decisions or the design optimization process. In this paper we discuss two examples—the design...

Sparse Gaussian graphical models characterize sparse dependence relationships between random variables in a network. To estimate multiple related Gaussian graphical models on the same set of variables, we formulate a hierarchical model, which leads to an optimization problem with a nonconvex log-shift penalty function. We show that under mild conditions the optimization problem is convex despit...

The paper focuses on the task of generating the first m best solutions for a combinatorial optimization problem defined over a graphical model (e.g., the m most probable explanations for a Bayesian network). We show that the mbest task can be expressed within the unifying framework of semirings making known inference algorithms defined and their correctness and completeness for the m-best task ...

We approach the problem of estimating the parameters of a latent tree graphical model from a hierarchical tensor decomposition point of view. In this new view, the marginal probability table of the observed variables is treated as a tensor, and we show that: (i) the latent variables induce low rank structures in various matricizations of the tensor; (ii) this collection of low rank matricizatio...

In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarit...

In this paper the authors describe a model driven approach for the development of heuristic optimization algorithms. Based on a generic algorithm model, several operators are presented which can be used as algorithm building blocks. In combination with a graphical user interface, this approach provides an interactive and declarative way of engineering complex optimization heuristics. By this me...

In this paper, we review the role of probabilistic graphical models in artificial intelligence. We start by giving an account of the early years when there was important controversy about the suitability of probability for intelligent systems. We then discuss the main milestones for the foundations of graphical models starting with Pearl’s pioneering work. Some of the main techniques for proble...

We propose a technique that combines generative adversarial networks with probabilistic graphical models to explicitly model dependencies in structured distributions. Generative adversarial structured networks (GASNs) produce samples by passing random inputs through a neural network to construct the potentials of a graphical model; maximum a-posteriori inference in this graphical model then yie...