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.
Nonstationary Dual Averaging and Online Fair Allocation
Authors: Luofeng Liao, Yuan Gao, Christian Kroer
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,995 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,959 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 | Finally, numerical experiments show strong empirical performance of PACE against nonstationary inputs. In Appendix F we provide numerical experiments which corroborate the above theory and demonstrate the practical ef๏ฌciency of PACE under different data input models. |
| Researcher Affiliation | Academia | Luofeng Liao, Yuan Gao, Christian Kroer IEOR, Columbia University EMAIL |
| Pseudocode | Yes | Algorithmic details are displayed in Algorithm 1. ... The algorithmic details for DA are presented in Algorithm 2. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix F. |
| Open Datasets | No | If your work uses existing assets, did you cite the creators? [No] We use synthetic data. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix F. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix F. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers in the main text, nor is this information guaranteed by the checklist items without access to Appendix F. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix F. |