Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization
Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem our algorithms scale linearly in the number of dependencies and constraints.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Interleaved Gibbs Diffusion for Constrained Generation
We introduce Interleaved Gibbs Diffusion (IGD), a novel generative modeling framework for mixed continuous-discrete data, focusing on constrained generation problems. Prior works on discrete and continuous-discrete diffu…
DenoisingContinuous-time mean-variance portfolio selection under non-Markovian regime-switching model with random horizon
In this paper, we consider a continuous-time mean-variance portfolio selection with regime-switching and random horizon. Unlike previous works, the dynamic of assets are described by non-Markovian regime-switching models…
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in gene…
General ClassificationRelational ReasoningOn Learning Continuous Pairwise Markov Random Fields
We consider learning a sparse pairwise Markov Random Field (MRF) with continuous-valued variables from i.i.d samples. We adapt the algorithm of Vuffray et al. (2019) to this setting and provide finite-sample analysis rev…
regressionOnline Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biology. As datasets continue to grow, online…
Stochastic Optimization