Emerging directions in Bayesian computation

Cites Work

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Robust adaptive Metropolis algorithm with coerced acceptance rate
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Asymptotic normality and valid inference for Gaussian variational approximation
Extremal families and systems of sufficient statistics
Exponential convergence of Langevin distributions and their discrete approximations
Merging MCMC subposteriors through Gaussian-process approximations
The sample size required in importance sampling
Sampling from a log-concave distribution with projected Langevin Monte Carlo
Inference from iterative simulation using multiple sequences
Consistency of variational Bayes inference for estimation and model selection in mixtures
Convergence rates of posterior distributions.
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
An introduction to variational methods for graphical models
The landscape of empirical risk for nonconvex losses
A comprehensive survey and analysis of generative models in machine learning
Near-optimal coresets of kernel density estimates
\(\alpha\)-variational inference with statistical guarantees
Concentration of tempered posteriors and of their variational approximations
Convergence rates of variational posterior distributions
A framework for adaptive MCMC targeting multimodal distributions
Distributed statistical estimation and rates of convergence in normal approximation
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Double-parallel Monte Carlo for Bayesian analysis of big data
Nonparametric Bayesian model selection and averaging
Distributed function estimation: adaptation using minimal communication
Randomized Algorithms for Matrices and Data
Graphical Models, Exponential Families, and Variational Inference
Rao-Blackwellisation of sampling schemes
Optimal Scaling of Discrete Approximations to Langevin Diffusions
Split-and-Augmented Gibbs Sampler—Application to Large-Scale Inference Problems
Scalable Bayes under Informative Sampling
The Continuum-Armed Bandit Problem
Optimal Subsampling for Large Sample Logistic Regression
Monte Carlo fusion
Global Consensus Monte Carlo
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Consensus Monte Carlo for Random Subsets Using Shared Anchors
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Private coresets
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Nonparametric Bayesian Aggregation for Massive Data
The Hastings algorithm at fifty
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Communication-Efficient Distributed Statistical Inference
Speeding Up MCMC by Efficient Data Subsampling
Frequentist Consistency of Variational Bayes
Simple, scalable and accurate posterior interval estimation
A unified framework for approximating and clustering data
Stochastic Gradient Markov Chain Monte Carlo
Optimal subsampling for quantile regression in big data
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data
Variational Bayes for High-Dimensional Linear Regression With Sparse Priors
An adaptive Metropolis algorithm
Cuts in Bayesian graphical models
Distributed Bayesian inference in massive spatial data
Online learning for the Dirichlet process mixture model via weakly conjugate approximation
Divide-and-conquer Bayesian inference in hidden Markov models
Minimax rate of distribution estimation on unknown submanifolds under adversarial losses
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression
An algorithm for distributed Bayesian inference
SwISS: a scalable Markov chain Monte Carlo divide-and-conquer strategy
Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets

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