Search Results for author:
Found 23 papers, 7 papers with code
Reproducible Parameter Inference Using Bagged Posteriors
Under model misspecification, it is known that Bayesian posteriors often do not properly quantify uncertainty about true or pseudo-true parameters.
A Targeted Accuracy Diagnostic for Variational Approximations
Variational Inference (VI) is an attractive alternative to Markov Chain Monte Carlo (MCMC) due to its computational efficiency in the case of large datasets and/or complex models with high-dimensional parameters.
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory.
Robust, Automated, and Accurate Black-box Variational Inference
RAABBVI adaptively decreases the learning rate by detecting convergence of the fixed--learning-rate iterates, then estimates the symmetrized Kullback--Leiber (KL) divergence between the current variational approximation and the optimal one.
Challenges for BBVI with Normalizing Flows
Current black-box variational inference (BBVI) methods require the user to make numerous design choices---such as the selection of variational objective and approximating family---yet there is little principled guidance on how to do so.
Challenges and Opportunities in High Dimensional Variational Inference
Our framework and supporting experiments help to distinguish between the behavior of BBVI methods for approximating low-dimensional versus moderate-to-high-dimensional posteriors.
Independent versus truncated finite approximations for Bayesian nonparametric inference
Bayesian nonparametric models based on completely random measures (CRMs) offers flexibility when the number of clusters or latent components in a data set is unknown.
Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations using stochastic optimization methods.
Validated Variational Inference via Practical Posterior Error Bounds
Finally, we demonstrate the utility of our proposed workflow and error bounds on a robust regression problem and on a real-data example with a widely used multilevel hierarchical model.
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome.
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines.
Data-dependent compression of random features for large-scale kernel approximation
Random feature maps (RFMs) and the Nystrom method both consider low-rank approximations to the kernel matrix as a potential solution.
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximation to the posterior distribution.
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
We develop an approach to scalable approximate GP regression with finite-data guarantees on the accuracy of pointwise posterior mean and variance estimates.
Random Feature Stein Discrepancies
Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness-of-fit testing.
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
We provide theoretical guarantees on the quality of point (MAP) estimates, the approximate posterior, and posterior mean and uncertainty estimates.
Quantifying the accuracy of approximate diffusions and Markov chains
As an illustration, we apply our framework to derive finite-sample error bounds of approximate unadjusted Langevin dynamics.
Coresets for Scalable Bayesian Logistic Regression
We demonstrate the efficacy of our approach on a number of synthetic and real-world datasets, and find that, in practice, the size of the coreset is independent of the original dataset size.
Risk and Regret of Hierarchical Bayesian Learners
Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ability to "share statistical strength."
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models.
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
In this note we provide detailed derivations of two versions of small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture models and the HDP hidden Markov model (HDP-HMM, a. k. a.
Infinite Structured Hidden Semi-Markov Models
This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models.
A Statistical Learning Theory Framework for Supervised Pattern Discovery
This paper formalizes a latent variable inference problem we call {\em supervised pattern discovery}, the goal of which is to find sets of observations that belong to a single ``pattern.''
