Post-Selection Distributional Model Evaluation

• URL: http://arxiv.org/abs/2603.23055v1
• Date: Tue, 24 Mar 2026 10:51:30 GMT
• Title: Post-Selection Distributional Model Evaluation
• Abstract summary: Post-selection distributional model evaluation (PS-DME)<n>PS-DME is a framework for statistically valid distributional model assessment after arbitrary data-dependent model pre-selection.<n>Results: PS-DME enables reliable comparison of candidate configurations across a range of reliability levels.
• Score: 31.006941545235396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 post-selection distributional model evaluation (PS-DME), a general framework for statistically valid distributional model assessment after arbitrary data-dependent model pre-selection. Building on e-values, PS-DME controls post-selection false coverage rate (FCR) for the distributional KPI estimates and is proved to be more sample efficient than a baseline method based on sample splitting. Experiments on synthetic data, text-to-SQL decoding with large language models, and telecom network performance evaluation demonstrate that PS-DME enables reliable comparison of candidate configurations across a range of reliability levels, supporting the statistically reliable exploration of performance--reliability trade-offs.

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