$\beta$-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers

TL;DR

This paper introduces a scalable, robust Bayesian inference method using $eta$-divergence and Riemannian coresets, effectively handling outliers in large datasets for diverse models.

Contribution

It proposes a novel variational inference approach that combines $eta$-divergence-based robustness with scalable Riemannian coreset techniques for large-scale Bayesian data summarization.

Findings

Outperforms existing methods in robustness to outliers

Effective on diverse datasets and models

Scalable to large datasets

Abstract

Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers. Despite the multiple benefits of Bayesian methods (such as uncertainty-aware predictions, incorporation of experts knowledge, and hierarchical modeling), the quality of classic Bayesian inference depends critically on whether observations conform with the assumed data generating model, which is impossible to guarantee in practice. In this work, we propose a variational inference method that, in a principled way, can simultaneously scale to large datasets, and robustify the inferred posterior with respect to the existence of outliers in the observed data. Reformulating Bayes theorem via the $\beta$-divergence, we posit a robustified pseudo-Bayesian posterior as the target of inference. Moreover, relying on the recent formulations of Riemannian coresets for scalable Bayesian inference, we propose a sparse variational approximation of the robustified posterior and an efficient stochastic black-box algorithm to construct it. Overall our method allows releasing cleansed data summaries that can be applied broadly in scenarios including structured data corruption. We illustrate the applicability of our approach in diverse simulated and real datasets, and various statistical models, including Gaussian mean inference, logistic and neural linear regression, demonstrating its superiority to existing Bayesian summarization methods in the presence of outliers.