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.
Improving Nash Social Welfare Approximations
Authors: Jugal Garg, Peter McGlaughlin
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,841 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,761 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 | We present novel de๏ฌnitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation in a way that provides most of the fairness properties of an integral max NSW allocation. |
| Researcher Affiliation | Academia | Jugal Garg and Peter Mc Glaughlin University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | Yes | Algorithm 1: Rounding Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper focuses on theoretical algorithmic design and proofs, and does not conduct experiments on datasets that would require specifying public access or citation details. |
| Dataset Splits | No | The paper focuses on theoretical algorithmic design and proofs, and does not conduct experiments on datasets that would require specifying validation splits. |
| Hardware Specification | No | The paper describes theoretical algorithmic work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical algorithmic work and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical algorithmic design and proofs, and does not describe an experimental setup with hyperparameters or training settings. |