1 code implementation • 18 Aug 2016 • Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter
Discrete optimization is a central problem in artificial intelligence.
no code implementations • 26 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).
no code implementations • 25 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.
no code implementations • 16 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.
no code implementations • 15 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).
no code implementations • 15 Jan 2014 • Robert Mateescu, Rina Dechter, Radu Marinescu
We provide two algorithms for compiling the AOMDD of a graphical model.
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.
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.
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.
no code implementations • 31 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.