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 [1].
Mechanism design augmented with output advice
Authors: George Christodoulou, Alkmini Sgouritsa, Ioannis Vlachos
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the full version of the paper we observe the behaviour of ˆρ, η in real-world datasets [26, 7, 37, 12, 16, 3]. |
| Researcher Affiliation | Academia | George Christodoulou Aristotle University of Thessaloniki and Archimedes/RC Athena, Greece EMAIL Alkmini Sgouritsa Athens University of Economics and Business and Archimedes/RC Athena, Greece EMAIL Ioannis Vlachos Athens University of Economics and Business and Archimedes/RC Athena, Greece EMAIL |
| Pseudocode | Yes | Mechanism 1 The Allocation Scaled Greedy mechanism Input: instance t Rn m, recommendation ˆa Rn m Output: a 1: rij 1 if ˆaij = 1, n β otherwise, (β [1, n]) 2: ij arg mini{rijtij} 3: if i = ij then aij = 1 else aij = 0, for each (i, j) N M |
| Open Source Code | Yes | Full code and data are provided in order to make the result reproduction possible. Data is open-source and helpful references and links are provided. |
| Open Datasets | Yes | In the full version of the paper we observe the behaviour of ˆρ, η in real-world datasets [26, 7, 37, 12, 16, 3]. |
| Dataset Splits | No | The paper mentions using real-world datasets but does not explicitly specify training, validation, or test splits in the main text. |
| Hardware Specification | Yes | While there is no need for intense computational power, the details of the computing machine s CPU are included in the experimental section. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, or other libraries/solvers) in its main text. |
| Experiment Setup | No | The paper discusses theoretical mechanisms and their properties but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings in the main text. |