9 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…
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…
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…
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…
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…
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…