Search Results for author:
Found 15 papers, 6 papers with code
Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
This allows us to study the performances of popular Metropolis-within-Gibbs schemes for non-conjugate hierarchical models, in high-dimensional regimes where both number of datapoints and parameters increase.
Partially factorized variational inference for high-dimensional mixed models
We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent variational inference (CAVI) algorithm for Gaussian targets.
Dimension-free mixing times of Gibbs samplers for Bayesian hierarchical models
Gibbs samplers are popular algorithms to approximate posterior distributions arising from Bayesian hierarchical models.
Improving multiple-try Metropolis with local balancing
We show both theoretically and empirically that this weight function induces pathological behaviours in high dimensions, especially during the convergence phase.
Robust leave-one-out cross-validation for high-dimensional Bayesian models
Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy.
Clustering consistency with Dirichlet process mixtures
Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering.
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
We derive an optimal choice of noise distribution for the Barker proposal, optimal choice of balancing function under a Gaussian noise distribution, and optimal choice of first-order locally-balanced algorithm among the entire class, which turns out to depend on the specific target distribution.
A fresh take on 'Barker dynamics' for MCMC
We provide a full derivation of the method from first principles, placing it within a wider class of continuous-time Markov jump processes.
Computation Methodology
Random Partition Models for Microclustering Tasks
Motivated by these issues, we propose a general class of random partition models that satisfy the microclustering property with well-characterized theoretical properties.
Methodology Statistics Theory Statistics Theory
Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models
Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions.
Methodology Computation
Scalable Importance Tempering and Bayesian Variable Selection
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling.
Scalable inference for crossed random effects models
We analyze the complexity of Gibbs samplers for inference in crossed random effect models used in modern analysis of variance.
Informed proposals for local MCMC in discrete spaces
There is a lack of methodological results to design efficient Markov chain Monte Carlo (MCMC) algorithms for statistical models with discrete-valued high-dimensional parameters.
Computation Probability
Unbiased approximations of products of expectations
This is wasteful and typically requires the number of particles to grow quadratically with the number of expectations.
Computation
Flexible Models for Microclustering with Application to Entity Resolution
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points.
