Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization

This repository contains the datasets, extracted features, and experimental results used in the study “Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization”.The folder organization is designed to clearly separate raw data, processed features, and experimental outputs, ensuring transparency and reproducibility.Top-level StructureProject- Adaptive estimation uncertainty quantification/│├── datasets/├── features/└── results/ datasets/This folder contains the original raw data used in the study, provided in two complementary forms:datasets/├── Nevergard_annotation-20251202T093830Z-1-001/│   └── Nevergard_annotation/│├── Nevergard_samples-20251202T093834Z-1-001/│   └── Nevergard_samples/ Nevergard_annotation/Contains annotation files associated with the dataset.These files define the annotations from the sample data.Nevergard_samples/Contains the sample data, the number of experimental runs.This is the primary input used for feature extraction and model training.Files are kept in their original format to preserve data integrity.features/This folder contains the extracted features derived from the samples and annotations datasets:features/└── all_features/ all_features/Stores the complete set of extracted features used in the experiments.These features serve as the input to all learning models. results/This directory contains the outputs of all experimental runs reported in the paper: results/├── Experiment_E1/├── Experiment_E2/├── Experiment_E3/├── Experiment_E4/└── Experiment_E5/  Experiment_E1 – Experiment_E5Each experiment folder corresponds to a distinct experimental configuration as presented in the paper:different uncertainty estimation strategies,different feature subsets,different model settings.This separation ensures that results from different experimental setups do not overlap and can be independently verified.