Measuring Learned Monotone Temporal Aggregation at Matched Admissibility
Risk regulation imposes directional constraints on scores; we adopt their strict per-input form -- the score monotone non-decreasing in every exposure input -- as a normative commitment. Deployed pipelines -- monotone hand-crafted aggregates feeding sign-constrained gradient boosting -- already satisfy it by composition, so constrained-versus-unconstrained comparisons price a guarantee the incumbent has for free. We instead hold admissibility fixed on both sides and measure what learning the aggregation is worth. Our instrument is a recurrent network whose state is classical risk statistics (an exponentially weighted moving average and a high-water mark with learned transforms), monotone ...
| Authors | Yew Lee Tan |
|---|---|
| Primary category | cs.LG |
| Published | 4 October 2026 |
| Last updated at source | 8 October 2026 |
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