12 accepted papers
We introduce learning to condition (L2C), a scalable, data-driven framework for accelerating Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs)—a fundamentally intractable problem. L2C trains a neural network to score variable-value assignments based on their utility…
Our paper builds on the recent trend of using neural networks trained with self-supervised or supervised learning to solve the Most Probable Explanation (MPE) task in discrete graphical models. At inference time, these networks take an evidence assignment as input and generate the most likely assign…
We propose a novel neural networks based approach to efficiently answer arbitrary Most Probable Explanation (MPE) queries—a well-known NP-hard task—in large probabilistic models such as Bayesian and Markov networks, probabilistic circuits, and neural auto-regressive models. By arbitrary MPE queries…
We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let $f$ and $g$ be two log-linear models defined over the sets $X$ and $Y$ of random variables. Given an assignment $x$ to all variables in $X$ (evidence or…
Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilistic representations, such as Bayesian and Markov networks, because PCs can solve marginal inference (MAR) tasks in time t…
Tractable models such as cutset networks and sum-product networks (SPNs) have become increasingly popular because they have superior predictive performance. Among them, cutset networks, which model the mechanics of Pearl’s cutset conditioning algorithm, demonstrate great scalability and prediction a…
Tractable probabilistic models such as cutset networks which admit exact linear time posterior marginal inference are often preferred in practice over intractable models such as Bayesian and Markov networks. This is because although tractable models, when learned from data, are slightly inferior to…
Tractable probabilistic models (TPMs) compactly represent a joint probability distribution over a large number of random variables and admit polynomial time computation of (1) exact likelihoods; (2) marginal probability distributions over a small subset of variables given evidence; and (3) in some…
Tractable probabilistic models (TPMs) are appealing because they admit polynomial-time inference for a wide variety of queries. In this work, we extend the cutset network (CN) framework, a powerful sub-class of TPMs that often outperforms probabilistic graphical models in terms of prediction accurac…
We propose several schemes for upper bounding the optimal value of the constrained most probable explanation (CMPE) problem. Given a set of discrete random variables, two probabilistic graphical models defined over them and a real number $q$, this problem involves finding an assignment of values to…
We consider the following constrained maximization problem in discrete probabilistic graphical models (PGMs). Given two (possibly identical) PGMs $M_1$ and $M_2$ defined over the same set of variables and a real number $q$, find an assignment of values to all variables such that the probability of t…
Tractable probabilistic models obviate the need for unreliable approximate inference approaches and as a result often yield accurate query answers in practice. However, most tractable models that achieve state-of-the-art generalization performance (measured using test set likelihood score) use laten…