Universal Adaptive Environment Discovery

2025-10-14

AI Summary

Machine learning models often fail to generalize across distributions due to reliance on spurious correlations. Method: This paper proposes the Unified Adaptive Environment Discovery (UAED) framework, a data-driven approach that automatically infers environment distributions without manual environment specification or grouping, and generates interpretable data transformation strategies to adaptively enforce invariant learning objectives—including Invariant Risk Minimization (IRM) and Risk Extrapolation (REx). Contribution/Results: Theoretically, UAED optimizes a robust objective—defined as the average over inferred environments—via PAC-Bayes generalization bounds. Empirically, on standard out-of-distribution generalization benchmarks, UAED significantly improves worst-case accuracy while maintaining competitive average accuracy, demonstrating strong robustness and generalization capability under unknown test environment distributions.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Learning & Optimization for ROB
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

An open problem in Machine Learning is how to avoid models to exploit spurious correlations in the data; a famous example is the background-label shortcut in the Waterbirds dataset. A common remedy is to train a model across multiple environments; in the Waterbirds dataset, this corresponds to training by randomizing the background. However, selecting the right environments is a challenging problem, given that these are rarely known a priori. We propose Universal Adaptive Environment Discovery (UAED), a unified framework that learns a distribution over data transformations that instantiate environments, and optimizes any robust objective averaged over this learned distribution. UAED yields adaptive variants of IRM, REx, GroupDRO, and CORAL without predefined groups or manual environment design. We provide a theoretical analysis by providing PAC-Bayes bounds and by showing robustness to test environment distributions under standard conditions. Empirically, UAED discovers interpretable environment distributions and improves worst-case accuracy on standard benchmarks, while remaining competitive on mean accuracy. Our results indicate that making environments adaptive is a practical route to out-of-distribution generalization.

Problem

• Avoiding model exploitation of spurious data correlations
• Learning adaptive environment distributions for robust training
• Improving worst-case accuracy without predefined environments

Innovation

• Learns distribution over adaptive data transformations
• Optimizes robust objectives across learned environments
• Generates adaptive variants without predefined groups