Search Results for author: Rina Dechter

Found 10 papers, 1 papers with code

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).

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.

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).

Clustering

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.

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.

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.

NeuroBE: NN Approximations to Bucket Elimination

no code implementations AAAI Workshop CLeaR 2022 Sakshi Agarwal, Kalev Kask, Alexander Ihler, Rina Dechter

A major limiting factor in graphical model inference is the complexity of computing the partition function.

Boosting AND/OR-Based Computational Protein Design: Dynamic Heuristics and Generalizable UFO

no code implementations31 Aug 2023 Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter

Scientific computing has experienced a surge empowered by advancements in technologies such as neural networks.

Protein Design

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