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].
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency
Authors: Anish Chakrabarty, Swagatam Das
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To close this gap, in this paper, we investigate the statistical properties of WAE. Firstly, we provide statistical guarantees that WAE achieves the target distribution in the latent space, utilizing the Vapnik Chervonenkis (VC) theory. The main result, consequently ensures the regeneration of the input distribution, harnessing the potential offered by Optimal Transport of measures under the Wasserstein metric. |
| Researcher Affiliation | Academia | Anish Chakrabarty Statistics and Mathematics Unit Indian Statistical Institute, Kolkata EMAIL Swagatam Das Electronics and Communication Sciences Unit Indian Statistical Institute, Kolkata EMAIL |
| Pseudocode | No | The paper contains mathematical proofs and theoretical analyses, but no structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is purely theoretical and does not involve experiments or datasets. The ethics statement indicates 'N/A' for experimental results, data, and code. |
| Dataset Splits | No | The paper is purely theoretical and does not involve empirical experiments with data. The ethics statement explicitly states 'N/A' regarding training details like data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware. The ethics statement confirms this by stating 'N/A' for compute resources used. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with specific version numbers. No information regarding software used for experiments is provided. |
| Experiment Setup | No | The paper is purely theoretical and does not describe an experimental setup. The ethics statement explicitly states 'N/A' regarding training details such as hyperparameters. |