Empirical Bayes Model Enables Robust Predictions Across Changing Environments
New method handles distribution shifts with a latent variable approach and amortized inference.
The paper introduces a novel framework for environment-robust representation learning, addressing a critical challenge in machine learning: models often fail when test environments differ from training ones. The authors assume that environments shift the distribution of an underlying latent variable, while the conditional relationships between that variable, observed covariates, and targets remain fixed. This mirrors real-world scenarios like hospitals with varying patient demographics but stable physiological-outcome links.
They formulate a Bayesian model and derive a variational objective that decomposes into per-environment terms plus a cross-environment balancing term. Using empirical Bayes to estimate the prior directly from data, they develop an amortized variational inference algorithm for posterior approximation. Experiments span three diverse domains: astronomical source identification (e.g., separating galaxies from stars), microbiome-based disease detection (using gut bacteria to predict conditions), and ICU sepsis prediction from electronic health records. In all cases, the method outperforms earlier environment-robust techniques, demonstrating strong generalization to unseen environments without requiring environment labels at test time.
- Uses empirical Bayes to automatically set priors from multi-environment data, avoiding manual tuning.
- Deploys a variational objective with a cross-environment balancing term that regularizes across groups.
- Validated on three real-world tasks: astronomy, microbiome disease detection, and ICU sepsis prediction.
Why It Matters
Reliable predictions across hospitals, cohorts, or data shifts are critical for AI in healthcare and science.