135 citations · 307 across the 31 of their papers we have counts for
15 papers · 1 filter
Learning to Guide Local Search for MPE Inference in Probabilistic Graphical Models
Brij Malhotra, Shivvrat Arya, Tahrima Rahman +1
Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, pl…
LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoning
Navapat Nananukul, Yue Zhang, Ryan Lee +5
High-assurance reasoning, particularly in critical domains such as law and medicine, requires conclusions that are accurate, verifiable, and explicitly grounded in evidence. This r…
Lifted Marginal MAP Inference
Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal +2
Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other. A number…
Lifted Region-Based Belief Propagation
David Smith, Parag Singla, Vibhav Gogate
Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Re…
Probabilistic Inference Modulo Theories
Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate +1
We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined vi…
Join-Graph Propagation Algorithms
Robert Mateescu, Kalev Kask, Vibhav Gogate +1
The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start…
Dynamic Blocking and Collapsing for Gibbs Sampling
Deepak Venugopal, Vibhav Gogate
In this paper, we investigate combining blocking and collapsing -- two widely used strategies for improving the accuracy of Gibbs sampling -- in the context of probabilistic graphi…
Structured Message Passing
Vibhav Gogate, Pedro Domingos
In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as alge…
Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
Vibhav Gogate, Rina Dechter
In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of cons…
Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
Vibhav Gogate, Rina Dechter, Bozhena Bidyuk +2
This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic…
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality
Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
Computing the probability of evidence even with known error bounds is NP-hard. In this paper we address this hard problem by settling on an easier problem. We propose an approximat…
AND/OR Importance Sampling
Vibhav Gogate, Rina Dechter
The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR spac…
Formula-Based Probabilistic Inference
Vibhav Gogate, Pedro Domingos
Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. S…
Probabilistic Theorem Proving
Vibhav Gogate, Pedro Domingos
Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower…
Approximation by Quantization
Vibhav Gogate, Pedro Domingos
Inference in graphical models consists of repeatedly multiplying and summing out potentials. It is generally intractable because the derived potentials obtained in this way can be…