Основные понятия
VISA optimizes variational inference efficiently by reusing samples, reducing computational cost.
Аннотация
Abstract
VISA introduces a method for approximate inference in computationally intensive models.
It extends importance-weighted forward-KL variational inference by employing sequential sample-average approximations.
Introduction
Bayesian analysis in simulation-based models is computationally costly due to repeated model evaluations.
Gradient-based methods are commonly used for inference in such models.
Data Extraction
"VISA can achieve comparable approximation accuracy to standard importance-weighted forward-KL variational inference with computational savings of a factor two or more."
Background
Variational Inference (VI) approximates an intractable target density with a tractable variational distribution.
SAA for Forward-KL Variational Inference
Sample-average approximations are used to approximate expected loss with a surrogate loss in optimization problems.
Experiments
VISA is compared to IWFVI in terms of inference quality and the number of model evaluations across different experiments.
Related Work
Recent work studies SAAs in the context of variational inference, focusing on optimizing reverse KL-divergence and using second-order methods.
Pickover Attractor Experiment
The Pickover attractor model is used to evaluate VISA's performance, showing stable convergence with fewer samples compared to IWFVI.
Статистика
"VISA can achieve comparable approximation accuracy to standard importance-weighted forward-KL variational inference with computational savings of a factor two or more."