On the Problem of Assigning PhD Grants
Authors: Katarína Cechlárová, Laurent Gourvès, Julien Lesca
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |