Sample Weight Averaging for Stable Prediction

2025-02-11

AI Summary

To address spurious correlations and the variance inflation and coefficient estimation bias induced by covariate shift in out-of-distribution (OOD) generalization—particularly under sample reweighting without environment labels or additional supervision—this paper proposes SAWA, a lightweight Sample-weight Averaging strategy. SAWA integrates importance weighting with propensity score concepts, employing weight smoothing and a theoretically grounded bias–variance trade-off analysis to provably reduce estimation variance while improving coefficient consistency. Experiments on synthetic and real-world datasets demonstrate that SAWA significantly outperforms existing reweighting methods, achieving lower estimation error and enhanced cross-environment prediction stability and OOD generalization. As a general-purpose, interpretable, and low-overhead solution, SAWA is especially suitable for risk-sensitive robust modeling scenarios.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• 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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The challenge of Out-of-Distribution (OOD) generalization poses a foundational concern for the application of machine learning algorithms to risk-sensitive areas. Inspired by traditional importance weighting and propensity weighting methods, prior approaches employ an independence-based sample reweighting procedure. They aim at decorrelating covariates to counteract the bias introduced by spurious correlations between unstable variables and the outcome, thus enhancing generalization and fulfilling stable prediction under covariate shift. Nonetheless, these methods are prone to experiencing an inflation of variance, primarily attributable to the reduced efficacy in utilizing training samples during the reweighting process. Existing remedies necessitate either environmental labels or substantially higher time costs along with additional assumptions and supervised information. To mitigate this issue, we propose SAmple Weight Averaging (SAWA), a simple yet efficacious strategy that can be universally integrated into various sample reweighting algorithms to decrease the variance and coefficient estimation error, thus boosting the covariate-shift generalization and achieving stable prediction across different environments. We prove its rationality and benefits theoretically. Experiments across synthetic datasets and real-world datasets consistently underscore its superiority against covariate shift.

Problem

• Addresses Out-of-Distribution generalization challenges
• Reduces variance in sample reweighting methods
• Enhances stable prediction under covariate shift

Innovation

• Sample Weight Averaging (SAWA)
• Reduces variance and estimation error
• Enhances covariate-shift generalization