Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inverse Problems for Gradual Semantics
Authors: Nir Oren, Bruno Yun, Srdjan Vesic, Murilo Baptista
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation is detailed in Section 5. We evaluated each of the strategies discussed in Section 3.3 over directed scale-free, small world (Erdos-Renyi), and complete graphs of different sizes. |
| Researcher Affiliation | Academia | 1University of Aberdeen 2CNRS, Univ. Artois, CRIL, France |
| Pseudocode | Yes | Algorithm 1 The bisection method. Algorithm 2 Computing arguments minimal upper bounds |
| Open Source Code | Yes | Source code for our algorithm and evaluation can be found on Git Hub at https://github.com/jhudsy/numerical inverse. |
| Open Datasets | No | The paper evaluates on 'directed scale-free, small world (Erdos-Renyi), and complete graphs of different sizes' which appear to be generated for the experiments, rather than being a named, publicly accessible dataset with explicit access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper describes creating a 'simple target preference ordering' and evaluating strategies, but does not mention specific training, validation, or test dataset splits, percentages, or cross-validation schemes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, memory amounts, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9), used for the experiments. |
| Experiment Setup | Yes | As part of our evaluation, we ran 10, 100 and 2000 iterations of the bisection method for each argument... Table 2 describes the remaining parameters used in our evaluation. Parameters used in our evaluation: ΞΆ: 1, Graph Size: 10, 20, ..., 150, Runs per graph size: 15, Erdos-Renyi probability: 0.1, 0.3, 0.5, 0.7, Maximum relative error: 0.001, Bisection method iterations: 10, 100, 2000, Bisection method Ο΅: 0.001, Maximum bisection method calls: 1000. |