A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New methods for tuning alpha in Gibbs posteriors improve speed and accuracy.
Paper introduces a new sampling method for Bayesian inference.
Bayesian framework for image inversion using regularization by denoising.
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widely used for inference in these models. However, both methods are restricted to limited types of transitions and suffer from torpid mixing and…
Novel Bayesian framework for Poisson inverse problems using Bregman geometry.
The hierarchical Dirichlet process (HDP) has become an important Bayesian nonparametric model for grouped data, such as document collections. The HDP is used to construct a flexible mixed-membership model where the number of components is determined by the data. As for most Bayesian nonparametric models, exact posterio…
Gibbs sampling is a Markov chain Monte Carlo method that is often used for learning and inference on graphical models. Minibatching, in which a small random subset of the graph is used at each iteration, can help make Gibbs sampling scale to large graphical models by reducing its computational cost. In this paper, we p…
DiGS improves sampling from multi-modal distributions.
A new algorithm for sampling from complex distributions.
New Gibbs sampling method improves MCMC efficiency.
Introduces HMC method for sampling Gibbs densities.
We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models. Collapsed Gibbs is a popular MCMC method, which integrates out variables in the posterior to improve mixing. Unfortunately for many complex models, integrating out these variables is either analytically or co…
The pairwise influence matrix of Dobrushin has long been used as an analytical tool to bound the rate of convergence of Gibbs sampling. In this work, we use Dobrushin influence as the basis of a practical tool to certify and efficiently improve the quality of a discrete Gibbs sampler. Our Dobrushin-optimized Gibbs samp…
We propose a restricted collapsed draw (RCD) sampler, a general Markov chain Monte Carlo sampler of simultaneous draws from a hierarchical Chinese restaurant process (HCRP) with restriction. Models that require simultaneous draws from a hierarchical Dirichlet process with restriction, such as infinite Hidden markov mod…
Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posterior distribution but are expensive for large datasets and high-dimensional models. A standard approach to mitigate this complexity consists i…
Gibbs sampling is the de facto Markov chain Monte Carlo method used for inference and learning on large scale graphical models. For complicated factor graphs with lots of factors, the performance of Gibbs sampling can be limited by the computational cost of executing a single update step of the Markov chain. This cost …
New method transfers instances between domains using Gibbs Sampling and RBM.
The paper proposes a Gibbs sampler for neural network posterior sampling.
The paper tackles sampling from Gibbs measures with constrained support, providing a sampling guarantee.
The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, that is entirely deterministic. We prove that herded Gibbs has an convergence rate for models with independent variables and for ful…
New Gibbs sampling reduces GLMB filtering complexity to linear time.
Bayesian inference for Levy density with Gibbs posterior in discrete sampling.
Improved Bayesian regression for large datasets using multilevel Gibbs sampling.
New model estimates Gibbs free energies using machine learning and isobaric-isothermal flows.
Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning, and statistics, employed to draw samples from complicated high-dimensional posterior distributions. The key point for the suc…
A fundamental task in machine learning and related fields is to perform inference on Bayesian networks. Since exact inference takes exponential time in general, a variety of approximate methods are used. Gibbs sampling is one of the most accurate approaches and provides unbiased samples from the posterior but it has hi…
Gibbs sampler mixes quickly for certain smooth distributions.
GDiff tackles blind denoising with Gibbs sampling and Monte Carlo inference.
Improved MALA method for neural networks uncertainty quantification.
Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an onli…
GIST adapts HMC by tuning parameters based on position and momentum.
New method improves sampling from noisy energy models.
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce …
We analyze Gibbs-based transfer learning algorithms using information theory.
Adaptive sampling for multimodal distributions converges faster than classical methods.
A fast Gibbs sampler for Bayesian HMMs with missing data.
New algorithm improves mixing in Bayesian mixture models.
New diagnostic tool for assessing approximate Bayesian inference.
Improved sampling for Bayesian neural networks reduces vanishing acceptance rates and increases predictive accuracy.
Discrete diffusion models improve text and image inference.
New methods improve sampling from complex dynamical models.
Study birth-death dynamics for sampling Gibbs measures with nonconvex potentials.
The latent Dirichlet allocation (LDA) model is a widely-used latent variable model in machine learning for text analysis. Inference for this model typically involves a single-site collapsed Gibbs sampling step for latent variables associated with observations. The efficiency of the sampling is critical to the success o…
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. Ho…
New MCMC methods improve efficiency for large network inference.
We study the Gibbs sampling algorithm for continuous determinantal point processes. We show that, given a warm start, the Gibbs sampler generates a random sample from a continuous -DPP defined on a -dimensional domain by only taking number of steps. As an application, we design an algorithm to ge…
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise graphical models with strictly positive factors that requires almost no preprocessing and is easy …
This work analyzes Gibbs samplers for Bayesian hierarchical models without dimensionality constraints.