X Zhang, Y Li, W Li, K Guo… - … Conference on Machine …, 2022 - proceedings.mlr.press
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 …
B Guedj - arXiv preprint arXiv:1901.05353, 2019 - arxiv.org
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at …
F Zhang, C Gao - The Annals of Statistics, 2020 - JSTOR
We study convergence rates of variational posterior distributions for nonparametric and high- dimensional inference. We formulate general conditions on prior, likelihood and variational …
J Bai, Q Song, G Cheng - Advances in Neural Information …, 2020 - proceedings.neurips.cc
Sparse deep learning aims to address the challenge of huge storage consumption by deep neural networks, and to recover the sparse structure of target functions. Although …
In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates. To fix this issue, it has been suggested to replace the likelihood by a …
Bayesian models are powerful tools for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly …
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the …
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models based on the Wasserstein distance and the total variation distance. Our first result …
Y Wang, D Blei - Advances in Neural Information …, 2019 - proceedings.neurips.cc
Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC) for Bayesian posterior inference. Though popular, VB comes with few theoretical guarantees …