Search Results for author: Rina Dechter

Found 8 papers, 1 papers with code

Counting the Optimal Solutions in Graphical Models

no code implementations NeurIPS 2019 Radu Marinescu, Rina Dechter

We introduce #opt, a new inference task for graphical models which calls for counting the number of optimal solutions of the model.

Dynamic Importance Sampling for Anytime Bounds of the Partition Function

no code implementations NeurIPS 2017 Qi Lou, Rina Dechter, Alexander T. Ihler

Our algorithm combines and generalizes recent work on anytime search and probabilistic bounds of the partition function.

Probabilistic Inference Modulo Theories

no code implementations26 May 2016 Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter

We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers).

Active Tuples-based Scheme for Bounding Posterior Beliefs

no code implementations16 Jan 2014 Bozhena Bidyuk, Rina Dechter, Emma Rollon

The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables.

Join-Graph Propagation Algorithms

no code implementations15 Jan 2014 Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP).

AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models

no code implementations15 Jan 2014 Robert Mateescu, Rina Dechter, Radu Marinescu

We provide two algorithms for compiling the AOMDD of a graphical model.

Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (2006)

no code implementations25 Aug 2012 Rina Dechter, Thomas S. Richardson

This is the Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, which was held in Cambridge, MA, July 13 - 16 2006.

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