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.
Optimal Efficiency-Envy Trade-Off via Optimal Transport
Authors: Steven Yin, Christian Kroer
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 15,468 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,553 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our solution with both artificial data, and simulated data from a realistic simulator for blood donor matching developed by [21]. Finally, in Figure 4 we investigate the quality of the empirical solutions as the sample size increases. It can been seen that the approximation gap decreases faster than the theoretical rate, confirming our sample complexity bound in Theorem 2. |
| Researcher Affiliation | Academia | Steven Yin Department of Industrial Engineering and Operations Research Columbia University New York, NY 10027 EMAIL Christian Kroer Department of Industrial Engineering and Operations Research Columbia University New York, NY 10027 EMAIL |
| Pseudocode | Yes | Algorithm 1: Projected SGD for Envy Constrained Optimal Transport |
| Open Source Code | Yes | Our code is provided as supplemental material. |
| Open Datasets | Yes | We test our solution with both artificial data, and simulated data from a realistic simulator for blood donor matching developed by [21]. |
| Dataset Splits | No | The paper states in the checklist 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix A.3'. However, Appendix A.3 itself (which is not provided in the current text) or the main text does not explicitly provide percentages or counts for training/validation/test splits. |
| Hardware Specification | No | The paper's checklist indicates that hardware specifications are in Appendix A.3, which is not included in the provided text. No hardware details are mentioned in the main body of the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | No | The paper's checklist indicates that training details and hyperparameters are in Appendix A.3, which is not included in the provided text. No specific experimental setup details like hyperparameter values or training configurations are provided in the main body. |