Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fair and Efficient Allocations Without Obvious Manipulations
Authors: Alexandros Psomas, Paritosh Verma
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,874 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,783 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All our theorems and lemmas have proofs, either in the first 9 pages, or in the supplementary material. |
| Researcher Affiliation | Academia | Alexandros Psomas Purdue University EMAIL Paritosh Verma Purdue University EMAIL |
| Pseudocode | Yes | Mechanism 1 sequentially considers four cases based on the sets {Dj}n j=1 and the values {Rj}n j=1, to find a temporary allocation A . See Appendix D.2 for the pseudo-code. |
| Open Source Code | No | The paper explicitly states N/A (Not Applicable) for the question regarding code availability in the checklist: "Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]" |
| Open Datasets | No | The paper explicitly states N/A (Not Applicable) for questions related to experiments, including data splits and training details: "Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]". |
| Dataset Splits | No | The paper explicitly states N/A (Not Applicable) for questions related to experiments, including data splits and training details: "Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]". |
| Hardware Specification | No | The paper explicitly states N/A (Not Applicable) for questions related to experiments and compute resources: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]". |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithmic design. It does not describe any specific software implementations or dependencies with version numbers, as it does not report on empirical experiments. |
| Experiment Setup | No | The paper explicitly states N/A (Not Applicable) for questions related to experiments and training details: "Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]". |