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].
Proportionally Fair Online Allocation of Public Goods with Predictions
Authors: Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, Nisarg Shah
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design online algorithms for the fair allocation of public goods to a set of N agents over a sequence of T rounds and focus on improving their performance using predictions. The algorithm's performance is measured using a proportional fairness objective, which informally demands that every group of agents be rewarded proportional to its size and the cohesiveness of its preferences. We show that no algorithm can achieve better than Θ(T/B) proportional fairness without predictions. With reasonably accurate predictions, the situation improves significantly, and Θ(log(T/B)) proportional fairness is achieved. |
| Researcher Affiliation | Academia | Siddhartha Banerjee1 , Vasilis Gkatzelis2 , Safwan Hossain3 , Billy Jin1 , Evi Micha4 and Nisarg Shah4 1Cornell University 2Drexel University 3Harvard University 4University of Toronto EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Set-Aside Greedy Algorithm for Batched Public Goods |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the public availability of source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments on datasets, thus no information on dataset availability is provided. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments on datasets, thus no information on training/validation/test splits is provided. |
| Hardware Specification | No | This paper is theoretical and does not mention any hardware specifications used for running experiments. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations. |