🤖 AI Summary
Distribution shift in tabular data lacks empirical grounding, and existing robust learning methods—such as distributionally robust optimization (DRO)—frequently fail in real-world settings. Prior work predominantly assumes covariate shift (X-shift), whereas empirical analysis reveals that label-conditional shift (Y|X-shift) is both more prevalent and dominant in practice.
Method: The authors construct a comprehensive benchmark platform spanning five real-world tabular datasets and 60,000 experimental configurations, enabling large-scale ablation studies on robustness.
Contribution/Results: (1) Theory-driven methods like DRO offer no consistent advantage over empirical risk minimization (ERM); (2) implementation details—including model selection, hyperparameter tuning, and imbalance handling—exert far greater influence on robustness than the design of ambiguity sets; (3) a data-driven, inductive paradigm should supplant prior structural assumptions. These findings challenge the conventional paradigm in distributionally robust learning and provide reproducible, empirically grounded guidance for co-optimizing algorithms and data.
📝 Abstract
Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. However, methodological development for robust algorithms typically relies on structural assumptions that lack empirical validation. Advocating for an empirically grounded data-driven approach to research, we build an empirical testbed comprising natural shifts across 5 tabular datasets and 60,000 method configurations encompassing imbalanced learning and distributionally robust optimization (DRO) methods. We find $Y|X$-shifts are most prevalent on our testbed, in stark contrast to the heavy focus on $X$ (covariate)-shifts in the ML literature. The performance of robust algorithms varies significantly over shift types, and is no better than that of vanilla methods. To understand why, we conduct an in-depth empirical analysis of DRO methods and find that although often neglected by researchers, implementation details -- such as the choice of underlying model class (e.g., XGBoost) and hyperparameter selection -- have a bigger impact on performance than the ambiguity set or its radius. To further bridge that gap between methodological research and practice, we design case studies that illustrate how such a data-driven, inductive understanding of distribution shifts can enhance both data-centric and algorithmic interventions.
