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..
Algorithmics of Egalitarian versus Equitable Sequences of Committees
Authors: Eva Michelle Deltl, Till Fluschnik, Robert Bredereck
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the parameterized complexity of finding such committees for the parameters n, m, k, τ, x, and y, as well as combinations thereof. |
| Researcher Affiliation | Academia | Eva Michelle Deltl1 , Till Fluschnik1,2 and Robert Bredereck2 1Technische Universit at Berlin, Faculty IV, Algorithmics and Computational Complexity 2TU Clausthal, Institut f ur Informatik EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: FPT-algorithm for PE-GCSE parameterized by k + τ on input (A, C, U, (kt)t, (xt)t, (ya)a). |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or statements about releasing its code. |
| Open Datasets | No | The paper is theoretical and does not involve data or datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with specific version numbers for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |