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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Non-asymptotic Error Bounds for Bidirectional GANs
Authors: Shiao Liu, Yunfei Yang, Jian Huang, Yuling Jiao, Yang Wang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive nearly sharp bounds for the bidirectional GAN (Bi GAN) estimation error under the Dudley distance between the latent joint distribution and the data joint distribution with appropriately specified architecture of the neural networks used in the model. To the best of our knowledge, this is the first theoretical guarantee for the bidirectional GAN learning approach. |
| Researcher Affiliation | Academia | Shiao Liu Department of Statistics and Actuarial Science, University of Iowa Iowa City, IA 52242, USA EMAIL Yunfei Yang Department of Mathematics, The Hong Kong University of Science and Technology Clear Water Bay, Hong Kong, China EMAIL Jian Huang Department of Statistics and Actuarial Science, University of Iowa Iowa City, IA 52242, USA EMAIL Yuling Jiao School of Mathematics and Statistics, Wuhan University Wuhan, Hubei, China 430072 EMAIL Yang Wang Department of Mathematics, The Hong Kong University of Science and Technology Clear Water Bay, Hong Kong, China EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | No experiments were conducted or described in the paper, hence no datasets were used or made available. |
| Dataset Splits | No | No experiments were conducted or described in the paper, hence no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | No experiments were conducted or described in the paper, hence no hardware specifications are provided. |
| Software Dependencies | No | No experiments were conducted or described in the paper, hence no software dependencies or versions are specified. |
| Experiment Setup | No | No experiments were conducted or described in the paper, hence no experimental setup details like hyperparameters or training settings are provided. |