Post-Selection Distributional Model Evaluation
Formal model evaluation methods typically certify that a model satisfies a prescribed target key performance indicator (KPI) level. However, in many applications, the relevant target KPI level may not be known a priori, and the user may instead wish to compare candidate models by analyzing the full trade-offs between performance and reliability achievable at test time by the models. This task, requiring the reliable estimate of the test-time KPI distributions, is made more complicated by the fact that the same data must often be used both to pre-select a subset of candidate models and to estimate their KPI distributions, causing a potential post-selection bias. In this work, we introduce ...
| Authors | Amirmohammad Farzaneh, Osvaldo Simeone |
|---|---|
| Primary category | stat.ML |
| Published | 24 March 2026 |
| Last updated at source | 7 May 2026 |
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