331 citations · 504 across the 25 of their papers we have counts for
26 papers
Large-scale empirical tuning and comparison of default optimizers for variational inference
Trevor Campbell, Jonathan H. Huggins, Kyurae Kim +1
Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization. In practice, the stochastic optimizers underpinning BBVI…
Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
Jinlin Lai, Charles C. Margossian, Daniel R. Sheldon
Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models and mixed-effect models. Effici…
Don't Disregard the Data for Lack of a Likelihood: Bayesian Synthetic Likelihood for Enhanced Multilevel Network Meta-Regression
Harlan Campbell, Charles C. Margossian, Jeroen P. Jansen +1
Multilevel network meta-regression (ML-NMR) enables population-adjusted indirect treatment comparisons by combining individual patient data (IPD) with aggregate data. When individu…
Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry
Charles C. Margossian, Isaac E. Rankin, Lawrence K. Saul
Variational inference (VI) approximates a target density by the best match in a family of tractable distributions. The best variational approximation is found by minimizing…
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
Ningyuan Huang, Richard Stiskalek, Jun-Young Lee +6
Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature…
EigenVI: score-based variational inference with orthogonal function expansions
Diana Cai, Chirag Modi, Charles C. Margossian +3
We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI). EigenVI constructs its variational approximations from orthogonal function expansions.…
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
Charles C. Margossian, Lawrence K. Saul
Given an intractable target density , variational inference (VI) attempts to find the best approximation from a tractable family . This is typically done by minimizing th…
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
Given an intractable distribution , the problem of variational inference (VI) is to find the best approximation from some more tractable family . Commonly, one chooses to…
Listening to the Noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana +1
In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the dist…
Batch and match: black-box variational inference with a score-based divergence
Diana Cai, Chirag Modi, Loucas Pillaud-Vivien +4
Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge…
For how many iterations should we run Markov chain Monte Carlo?
Charles C. Margossian, Andrew Gelman
Standard Markov chain Monte Carlo (MCMC) admits three fundamental control parameters: the number of chains, the length of the warmup phase, and the length of the sampling phase. Th…
Variational Inference with Gaussian Score Matching
Chirag Modi, Charles Margossian, Yuling Yao +3
Variational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics. Typically, VI fits a simple paramet…
Amortized Variational Inference: When and Why?
Charles C. Margossian, David M. Blei
In a probabilistic latent variable model, factorized (or mean-field) variational inference (F-VI) fits a separate parametric distribution for each latent variable. Amortized variat…
General adjoint-differentiated Laplace approximation
Charles C. Margossian
The hierarchical prior used in Latent Gaussian models (LGMs) induces a posterior geometry prone to frustrate inference algorithms. Marginalizing out the latent Gaussian variable us…
The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference
Charles C. Margossian, Lawrence K. Saul
When factorized approximations are used for variational inference (VI), they tend to underestimate the uncertainty -- as measured in various ways -- of the distributions they are m…
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories
Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc +2
Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous probability distributions. In many cases, the underlying Hamiltonian dynamics exhibit a phenomenon of resonanc…
Efficient Automatic Differentiation of Implicit Functions
Charles C. Margossian, Michael Betancourt
Derivative-based algorithms are ubiquitous in statistics, machine learning, and applied mathematics. Automatic differentiation offers an algorithmic way to efficiently evaluate the…
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains
Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov +3
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such…
Efficient Sampling for Ising and Potts Models using Auxiliary Gaussian Variables
Charles C. Margossian, Chenyang Zhong, Sumit Mukherjee
Ising and Potts models are an important class of discrete probability distributions which originated from statistical physics and since then have found applications in several disc…
Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I
Charles C. Margossian, Yi Zhang, William R. Gillespie
Stan is an open-source probabilistic programing language, primarily designed to do Bayesian data analysis. Its main inference algorithm is an adaptive Hamiltonian Monte Carlo sampl…
Bayesian Workflow
Andrew Gelman, Aki Vehtari, Daniel Simpson +7
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilis…
Bayesian workflow for disease transmission modeling in Stan
Léo Grinsztajn, Elizaveta Semenova, Charles C. Margossian +1
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and oth…
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
Charles C. Margossian, Aki Vehtari, Daniel Simpson +1
Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as…