Probabilistic Inference Modulo Theories

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).

Anytime Exact Belief Propagation

no code implementations • 27 Jul 2017 • Gabriel Azevedo Ferreira, Quentin Bertrand, Charles Maussion, Rodrigo de Salvo Braz

In this paper we present work in progress on an Anytime Exact Belief Propagation algorithm that is very similar to Belief Propagation but is exact even for graphical models with cycles, while exhibiting soft short-circuiting, amortized constant time complexity in the model size, and which can provide probabilistic proof trees.

Probabilistic Programming

Exact Inference for Relational Graphical Models with Interpreted Functions: Lifted Probabilistic Inference Modulo Theories

no code implementations • 4 Sep 2017 • Rodrigo de Salvo Braz, Ciaran O'Reilly

Probabilistic Inference Modulo Theories (PIMT) is a recent framework that expands exact inference on graphical models to use richer languages that include arithmetic, equalities, and inequalities on both integers and real numbers.

10-shot image generation 3D-Aware Image Synthesis

Pearl: A Production-ready Reinforcement Learning Agent

1 code implementation • 6 Dec 2023 • Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu

Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.

reinforcement-learning Reinforcement Learning (RL)