Nonstationary Dual Averaging and Online Fair Allocation
Authors: Luofeng Liao, Yuan Gao, Christian Kroer
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 efficiency of PACE under different data input models. |
| Researcher Affiliation | Academia | Luofeng Liao, Yuan Gao, Christian Kroer IEOR, Columbia University {ll3530,yg254,ck294}@columbia.edu |
| 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. |