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.
Approximation and Convergence Properties of Generative Adversarial Learning
Authors: Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 14,725 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,417 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 | 2To the best of our knowledge, neither (3) or (4) was used in any GAN algorithm. However, since our focus in this paper is not implementing new algorithms, we leave experiments with this formulation for future work. |
| Researcher Affiliation | Collaboration | Shuang Liu University of California, San Diego EMAIL Olivier Bousquet Google Brain EMAIL Kamalika Chaudhuri University of California, San Diego EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | 2To the best of our knowledge, neither (3) or (4) was used in any GAN algorithm. However, since our focus in this paper is not implementing new algorithms, we leave experiments with this formulation for future work. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments, therefore no datasets are used or made publicly available for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no specific hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore no specific experimental setup details or hyperparameters are provided. |