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..
On the Problem of Assigning PhD Grants
Authors: Katarína Cechlárová, Laurent Gourvès, Julien Lesca
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the problem of assigning Ph D grants. Without using probability to model uncertainty, we study the possibility of designing protocols of exchanges between the students and the university in order to construct a matching which is as close as possible to the optimal one i.e., the best achievable matching without uncertainty. This framework relies on approximation algorithms [Vazirani, 2001] but the barrier is the lack of information instead of the computational complexity. |
| Researcher Affiliation | Academia | 1P.J. ˇSaf arik University, Koˇsice, Slovakia 2Universit e Paris-Dauphine, PSL, CNRS, LAMSADE, Paris, France |
| Pseudocode | Yes | Algorithm 1 Optimal sequential protocol Input: Candidacy set C, initial matching M 0. |
| Open Source Code | No | The paper is theoretical and focuses on algorithm design and proofs, not on releasing implementation code. It does not mention open-sourcing any code. |
| Open Datasets | No | The paper is theoretical and uses small examples to illustrate concepts, but it does not involve training models on a dataset or using publicly available datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical validation on data. Thus, there are no mentions of validation splits or processes. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers, as it does not report on empirical experiments requiring such details. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs. It does not describe any empirical experimental setup, hyperparameters, or system-level training settings. |