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From Garbage to Gold: A Data-Architectural Theory of Predictive Robustness

arXiv:2603.12288v1 Announce Type: cross Abstract: Tabular machine learning presents a paradox: modern models achieve state-of-the-art performance using high-dimensional (high-D), collinear, error-prone data, defying the "Garbage In, Garbage Out" mantra. To help resolve this, we synthesize principles from Information Theory, Latent Factor Models, and Psychometrics, clarifying that predictive robustness arises not solely from data cleanliness, but from the synergy between data architecture and model capacity. Partitioning predictor-space "noise" into "Predictor Error" and "Structural Uncertainty" (informational deficits from stochastic generative mappings), we prove that leveraging high-D sets of error-prone predictors asymptotically overcomes both types of noise, whereas cleaning a low-D set is fundamentally bounded by Structural Uncertainty. We demonstrate why "Informative Collinearity" (dependencies from shared latent causes) enhances reliability and convergence efficiency, and expla

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Terrence J. Lee-St. John, Jordan L. Lawson, Bartlomiej Piechowski-Jozwiak · · 1 min read · 14 views
#cs.LG #cs.AI #stat.ML

arXiv:2603.12288v1 Announce Type: cross Abstract: Tabular machine learning presents a paradox: modern models achieve state-of-the-art performance using high-dimensional (high-D), collinear, error-prone data, defying the "Garbage In, Garbage Out" mantra. To help resolve this, we synthesize principles from Information Theory, Latent Factor Models, and Psychometrics, clarifying that predictive robustness arises not solely from data cleanliness, but from the synergy between data architecture and model capacity. Partitioning predictor-space "noise" into "Predictor Error" and "Structural Uncertainty" (informational deficits from stochastic generative mappings), we prove that leveraging high-D sets of error-prone predictors asymptotically overcomes both types of noise, whereas cleaning a low-D set is fundamentally bounded by Structural Uncertainty. We demonstrate why "Informative Collinearity" (dependencies from shared latent causes) enhances reliability and convergence efficiency, and explain why increased dimensionality reduces the latent inference burden, enabling feasibility with finite samples. To address practical constraints, we propose "Proactive Data-Centric AI" to identify predictors that enable robustness efficiently. We also derive boundaries for Systematic Error Regimes and show why models that absorb "rogue" dependencies can mitigate assumption violations. Linking latent architecture to Benign Overfitting, we offer a first step towards a unified view of robustness to Outcome Error and predictor-space noise, while also delineating when traditional DCAI's focus on label cleaning remains powerful. By redefining data quality from item-level perfection to portfolio-level architecture, we provide a theoretical rationale for "Local Factories" -- learning from live, uncurated enterprise "data swamps" -- supporting a deployment paradigm shift from "Model Transfer" to "Methodology Transfer'' to overcome static generalizability limitations.

Executive Summary

The article 'From Garbage to Gold' redefines the conventional wisdom about data quality in tabular machine learning by asserting that predictive robustness is not primarily derived from data cleanliness but from an interplay between data architecture and model capacity. Rather than eliminating noise, the authors argue that high-dimensional, collinear, error-prone data can asymptotically mitigate both predictor error and structural uncertainty through the synergy between dimensionality and latent structure. Informative collinearity, arising from shared latent causes, is shown to enhance reliability and efficiency. The paper introduces a novel framework that shifts the focus from item-level data perfection to portfolio-level architectural design, enabling robustness in 'data swamps' via proactive data-centric AI. This redefinition of data quality has significant implications for model deployment and training paradigms, moving from model transfer to methodology transfer.

Key Points

  • ▸ Predictive robustness arises from synergy between data architecture and model capacity, not solely from data cleanliness.
  • ▸ High-dimensional data, despite containing error-prone predictors, can asymptotically overcome noise due to latent structure interplay.
  • ▸ Informative collinearity (shared latent causes) enhances reliability and convergence efficiency, and increased dimensionality reduces latent inference burden.
  • ▸ The authors propose a shift from traditional data cleaning to proactive data-centric AI and redefine data quality as portfolio-level architectural design.

Merits

Conceptual Innovation

The paper offers a novel theoretical framework that challenges the 'Garbage In, Garbage Out' paradigm by presenting a mathematically grounded argument for robustness via architectural synergy.

Practical Relevance

The proposed 'Proactive Data-Centric AI' offers actionable guidance for enterprise data environments, enabling robustness without exhaustive data cleaning.

Demerits

Complexity of Application

The theoretical constructs—predictor error, structural uncertainty, and latent inference burden—may present challenges for implementation due to their abstract nature and lack of concrete operational metrics.

Limited Scope for Low-D Data

The argument for overcoming noise via dimensionality applies primarily to high-D scenarios; the paper acknowledges that low-D data are fundamentally bounded by structural uncertainty, limiting applicability.

Expert Commentary

The article represents a substantive advancement in the discourse on predictive robustness by shifting the analytical lens from data quality metrics to architectural integrity. The authors successfully bridge Information Theory, Latent Factor Models, and Psychometrics to articulate a coherent mechanism for why high-dimensional collinearity, rather than purity, supports robustness. This is particularly compelling in enterprise settings where data swamps are ubiquitous. The distinction between predictor error and structural uncertainty is a critical conceptual contribution. Moreover, the proposal to redefine data quality as portfolio-level architecture aligns with evolving data ecosystems where curated datasets are increasingly rare. However, the paper’s reliance on abstract theoretical constructs may limit uptake among practitioners accustomed to empirical validation over formal proofs. A more concrete taxonomy or illustrative case studies could bridge this gap. Overall, this work is a landmark in reorienting the conversation from cleaning to structuring—a paradigm shift with lasting implications for AI deployment.

Recommendations

  • ✓ Practitioners should incorporate architectural design principles—such as intentional collinearity and dimensionality optimization—into model development pipelines for robustness-first design.
  • ✓ Academic institutions and funding bodies should support empirical validation of architectural-robustness theories through standardized benchmarks that measure robustness across varying data architectures and dimensionalities.

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