BAYESIAN INVARIANCE ENVIRONMENT DATA

Luhuan Wu, Mingzhang Yin, Yixin Wang, John Patrick Cunningham, David Blei

Track: regular paper (up to 6 pages)

Keywords: invariance; multi-environment; bayesian modeling; variational inference

TL;DR: Develop a scalable bayesian model for invariance modeling of multi-environment data

Abstract: Identifying invariant features – those that stably predict the outcome across diverse environments – is crucial for improving model generalization and uncovering causal mechanisms. While previous methods primarily address this problem through hypothesis testing or regularized optimization, they often lack a principled characterization of the underlying data generative process and struggle with high-dimensional data. In this work, we develop a Bayesian model that encodes an invariance assumption in the generative process of multi-environment data. Within this framework, we perform posterior inference to estimate the invariant features and establish theoretical guarantees on posterior consistency and contraction rates. To address the challenges in high-dimensional settings, we design a scalable variational inference algorithm. We demonstrate the superior inference accuracy and scalability of our method compared to existing approaches in simulations and a gene-perturbation study.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Presenter: ~Luhuan_Wu1

Submission Number: 45