Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

Federated learning faces huge challenges from model overfitting due to the lack of data and
statistical diversity among clients. To address these challenges, this paper proposes a novel …

Abstract Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI)
has emerged as a central computational approach to large-scale Bayesian inference …

Variational inference (VI) seeks to approximate a target distribution $\pi $ by an element of a
tractable family of distributions. Of key interest in statistics and machine learning is Gaussian …

ABSTRACT A key challenge for modern Bayesian statistics is how to perform scalable
inference of posterior distributions. To address this challenge, variational Bayes (VB) …

We provide the first convergence guarantee for black-box variational inference (BBVI) with
the reparameterization gradient. While preliminary investigations worked on simplified …

Finding unified complexity measures and algorithms for sample-efficient learning is a central
topic of research in reinforcement learning (RL). The Decision-Estimation Coefficient (DEC) …

We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian
model selection priors in sparse high-dimensional linear regression. Under compatibility …

We study convergence rates of variational posterior distributions for nonparametric and high-
dimensional inference. We formulate general conditions on prior, likelihood and variational …

Black-box variational inference is widely used in situations where there is no proof that its
stochastic optimization succeeds. We suggest this is due to a theoretical gap in existing …