Measuring Learned Monotone Temporal Aggregation at Matched Admissibility
Read the original on arXiv Machine Learning →The paper evaluates a recurrent network that learns monotone temporal aggregation for risk scoring, comparing it to hand‑crafted monotone aggregates used in deployed pipelines. Functional regression shows the learned channel can reproduce the geometrically weighted separable family of engineered statistics with Spearman ρ ≥ 0.996, while it struggles with consecutivity, time localization, and exposure floor, all of which can be fixed with an admissible basis. In practice, the learned aggregator matches or exceeds engineered counterparts in full‑sample scenarios, offers modest AUC gains in certain decay regions, and maintains performance where tree‑ensemble scores flatten.
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