113 citations · 294 across the 25 of their papers we have counts for
3 papers · 1 filter
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
Jeffrey Negrea, Jun Yang, Haoyue Feng +2
The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this…
A Framework for Improving the Reliability of Black-box Variational Inference
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari +1
Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approxi…
Calibrated Model Criticism Using Split Predictive Checks
Jiawei Li, Jonathan H. Huggins
Checking how well a fitted model explains the data is one of the most fundamental parts of a Bayesian data analysis. However, existing model checking methods suffer from trade-offs…