Notice: 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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
On Frank-Wolfe and Equilibrium Computation
Authors: Jacob D. Abernethy, Jun-Kun Wang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explore a few such resulting methods, and provide experimental results to demonstrate correctness and efficiency. |
| Researcher Affiliation | Academia | Jacob Abernethy Georgia Institute of Technology EMAIL Jun-Kun Wang Georgia Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta Algorithm for equilibrium computation |
| Open Source Code | No | The paper states 'provide experimental results to demonstrate correctness and efficiency' but does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions 'experimental results' but does not specify the datasets used for these experiments, nor does it provide any concrete access information (links, citations) for public datasets. |
| Dataset Splits | No | The paper does not specify exact dataset split percentages, sample counts, or reference predefined splits. It mentions 'experimental results' but provides no details on how data was partitioned for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, required to replicate the experiments. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings for the experiments. |