Contents1 1 PrinCut 22 1.1 How to use PrinCut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Do not distribute. 1 PrinCut22 1.1 How to use PrinCut23 The PrinCut GUI is shown in Figure 1. PrinCut is a MATLAB app, and its package is also provided24 in the supplementary. The left shows raw data without annotation. The right shows both raw data and annotation overlay.

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.

First, wepresent a unified framework that encompasses previous methods.

Recently,therehasbeenanincreasing interestindevelopment of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI.

The first two permit using subgradients andstochastic gradients, andareshowntoensureaO(1/ t) convergencerate.