Environment Inference for Invariant Learning (2010.07249v5)

Abstract: Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training examples are partitioned into "domains" or "environments". Our focus is on the more common setting where such partitions are not provided. We propose EIIL, a general framework for domain-invariant learning that incorporates Environment Inference to directly infer partitions that are maximally informative for downstream Invariant Learning. We show that EIIL outperforms invariant learning methods on the CMNIST benchmark without using environment labels, and significantly outperforms ERM on worst-group performance in the Waterbirds and CivilComments datasets. Finally, we establish connections between EIIL and algorithmic fairness, which enables EIIL to improve accuracy and calibration in a fair prediction problem.

• The paper presents EIIL, a method that first infers environment partitions from a pre-trained model to drive invariant feature learning.
• It demonstrates significant performance gains on benchmarks like CMNIST and Waterbirds, improving robustness under distribution shifts.
• The framework enhances fairness by aligning worst-case group performances without the need for explicit domain labels.

Environment Inference for Invariant Learning: An Expert Overview

The paper entitled "Environment Inference for Invariant Learning" by Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel, explores a novel methodological framework to address the challenge of learning models that maintain performance across distribution shifts effectively. The authors introduce a technique named Environment Inference for Invariant Learning (EIIL), aiming to relax one of the common assumptions in domain-invariant learning—namely, that environment or domain partition labels are available during training.

Problem Context

Invariant learning focuses on isolating features that are invariant across various domains, attempting to mitigate the degradation in model performance frequently observed during distribution shifts. Real-world scenarios rarely come labeled with environment information, prompting the necessity for models that can infer such partitions independently to improve robustness and fairness. Previous methods, such as IRM, have been limited due to their dependence on pre-defined environment labels, which are not always accessible.

Core Contributions

EIIL is presented as a general framework that employs a two-stage process consisting of environment inference followed by invariant learning:

1. Environment Inference (EI): This phase hypothesizes environment assignments derived from the statistical variability in the output of a pre-trained reference model. The EI process seeks to identify partitions that maximize divergence, thereby enabling effective invariant learning.
2. Invariant Learning (IL): Using environments inferred in the EI phase, various existing invariant learning techniques can be applied to train models that are robust to distribution shifts.

The authors demonstrate that EIIL outperforms existing invariant learning methods on benchmark datasets, such as CMNIST, where EIIL attains superior test accuracy, as well as on the Waterbirds dataset, where it improves upon ERM's worst-group performance despite the absence of explicit environment labels.

Analytical Insights

The paper establishes EIIL's theoretical grounding by investigating the dependencies of the framework. Specifically, environments inferred by EIIL are shown to crucially hinge on the biases of the reference model. The optimal environments discovered—whether they highlight spurious correlations utilized by a reference model—are pivotal for enhancing worst-case distribution resilience, effectively encouraging models to focus on domain-invariant features.

Additionally, EIIL's flexibility in relation to fairness is contemplated, highlighting its utility in aligning predictive performance across demographically defined subgroups without demographic labels. Such connections demonstrate EIIL's potential to intertwine fairness metrics with general invariant learning principles, extending its applicability.

Practical Implications and Future Directions

The primary practical implication of EIIL lies in its ability to improve domain generalization and algorithmic fairness in situations where manual domain labels are either costly or impossible to obtain. This marks a significant step forward as it effectively amplifies the applicability of invariant learning approaches. Additionally, its robustness under varied conditions in benchmark evaluations suggests that EIIL could substantially refine machine learning models' competency in complex real-world scenarios characterized by mutable data landscapes.

Looking ahead, future research could delve into optimizing EIIL's computational efficiency, extending its scalability to larger datasets, and further examining the conditions under which various reference models result in optimal environment inferences. Moreover, the integration of amortized inference techniques could enhance the practical deployment of EIIL, particularly in streamlining the computational overhead associated with dynamic environment inference.

Conclusion

In summary, this paper presents a robust and effective method for environment inference in invariant learning, masterfully navigating the challenges associated with domain label unavailability. By doing so, EIIL advances the field's ability to develop models that are resilient to distribution shifts and highlights promising directions for future research in not only domain generalization but also in fairness-aware machine learning.