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Variational Inference
Variational inference has become an important research topic in machine learning. It transforms a posterior reasoning problem into an optimization...
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Stochastic variational inference for GARCH models
Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skewed t...
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Variational inference: uncertainty quantification in additive models
Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior...
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PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization
Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of...
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Implicitly adaptive optimal proposal in variational inference for Bayesian learning
Overdispersed black-box variational inference uses importance sampling to decrease the variance of the Monte Carlo gradient in variational inference....
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Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks
Stochastic variational inference is an efficient Bayesian inference technology for massive datasets, which approximates posteriors by using noisy...
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Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models
Multilevel Stein variational gradient descent is a method for particle-based variational inference that leverages hierarchies of surrogate target...
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Bayesian compositional regression with microbiome features via variational inference
The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other...
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Stochastic variational inference for scalable non-stationary Gaussian process regression
A natural extension to standard Gaussian process (GP) regression is the use of non-stationary Gaussian processes, an approach where the parameters of...
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Clustering functional data via variational inference
Among different functional data analyses, clustering analysis aims to determine underlying groups of curves in the dataset when there is no...
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Sum-of-Squares Relaxations for Information Theory and Variational Inference
We consider extensions of the Shannon relative entropy, referred to as f -divergences. Three classical related computational problems are typically...
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Amortized Variational Inference via Nosé-Hoover Thermostat Hamiltonian Monte Carlo
Sampling latents from the posterior distribution efficiently and accurately is a fundamental problem for posterior inference. Markov chain Monte...
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Variational Inference Driven Drug Protein Binding Prediction
The identification of drug-protein interactions (DPIs) is a key task in drug discovery, where drugs are chemical compounds and targets are proteins....
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The computational asymptotics of Gaussian variational inference and the Laplace approximation
Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior...
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VICTree - A Variational Inference Method for Clonal Tree Reconstruction
Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing...
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VI-DGP: A Variational Inference Method with Deep Generative Prior for Solving High-Dimensional Inverse Problems
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main...
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The Effect of Temporal Correlations on State Estimation Through Variational Bayesian Inference
Effective health monitoring in dynamic systems hinges on the proper estimation of the system’s state. As one of the most powerful methods of state...
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Kernel Bayesian nonlinear matrix factorization based on variational inference for human–virus protein–protein interaction prediction
Identification of potential human–virus protein–protein interactions (PPIs) contributes to the understanding of the mechanisms of viral infection and...
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Variational inference for semiparametric Bayesian novelty detection in large datasets
After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection...
