Jonathan H. Huggins | DeepAI

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∙ 02/24/2023

A Targeted Accuracy Diagnostic for Variational Approximations

Variational Inference (VI) is an attractive alternative to Markov Chain ...

0 Yu Wang, et al. ∙

∙ 07/25/2022

Statistical Inference with Stochastic Gradient Algorithms

Tuning of stochastic gradient algorithms (SGAs) for optimization and sam...

0 Jeffrey Negrea, et al. ∙

∙ 03/29/2022

Robust, Automated, and Accurate Black-box Variational Inference

Black-box variational inference (BBVI) now sees widespread use in machin...

0 Manushi Welandawe, et al. ∙

∙ 03/29/2022

Calibrated Model Criticism Using Split Predictive Checks

Checking how well a fitted model explains the data is one of the most fu...

0 Jiawei Li, et al. ∙

∙ 09/01/2020

Robust, Accurate Stochastic Optimization for Variational Inference

We consider the problem of fitting variational posterior approximations ...

8 Akash Kumar Dhaka, et al. ∙

∙ 07/24/2020

Robust and Reproducible Model Selection Using Bagged Posteriors

Bayesian model selection is premised on the assumption that the data are...

0 Jonathan H. Huggins, et al. ∙

∙ 12/15/2019

Using bagged posteriors for robust inference and model criticism

Standard Bayesian inference is known to be sensitive to model misspecifi...

1 Jonathan H. Huggins, et al. ∙

∙ 10/09/2019

Practical Posterior Error Bounds from Variational Objectives

Variational inference has become an increasingly attractive, computation...

23 Jonathan H. Huggins, et al. ∙

∙ 05/17/2019

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations

Due to the ease of modern data collection, applied statisticians often h...

0 Brian L Trippe, et al. ∙

∙ 05/16/2019

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions

Discovering interaction effects on a response of interest is a fundament...

3 Raj Agrawal, et al. ∙

∙ 10/09/2018

Data-dependent compression of random features for large-scale kernel approximation

Kernel methods offer the flexibility to learn complex relationships in m...

8 Raj Agrawal, et al. ∙

∙ 09/25/2018

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

Bayesian inference typically requires the computation of an approximatio...

24 Jonathan H. Huggins, et al. ∙

∙ 06/26/2018

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees

Gaussian processes (GPs) offer a flexible class of priors for nonparamet...

2 Jonathan H. Huggins, et al. ∙

∙ 06/20/2018

Random Feature Stein Discrepancies

Computable Stein discrepancies have been deployed for a variety of appli...

0 Jonathan H. Huggins, et al. ∙

∙ 09/26/2017

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

Generalized linear models (GLMs) -- such as logistic regression, Poisson...

0 Jonathan H. Huggins, et al. ∙

∙ 05/20/2016

Coresets for Scalable Bayesian Logistic Regression

The use of Bayesian methods in large-scale data settings is attractive b...

0 Jonathan H. Huggins, et al. ∙

∙ 05/20/2016

Quantifying the accuracy of approximate diffusions and Markov chains

Markov chains and diffusion processes are indispensable tools in machine...

0 Jonathan H. Huggins, et al. ∙

∙ 05/19/2015

Risk and Regret of Hierarchical Bayesian Learners

Common statistical practice has shown that the full power of Bayesian me...

0 Jonathan H. Huggins, et al. ∙

∙ 03/01/2015

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes

Markov jump processes (MJPs) are used to model a wide range of phenomena...

0 Jonathan H. Huggins, et al. ∙

∙ 12/31/2014

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models

In this note we provide detailed derivations of two versions of small-va...

0 Jonathan H. Huggins, et al. ∙

∙ 06/30/2014

Infinite Structured Hidden Semi-Markov Models

This paper reviews recent advances in Bayesian nonparametric techniques ...

0 Jonathan H. Huggins, et al. ∙

∙ 07/02/2013

A Statistical Learning Theory Framework for Supervised Pattern Discovery

This paper formalizes a latent variable inference problem we call super...

0 Jonathan H. Huggins, et al. ∙

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