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.
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
Authors: Seunghwan An, Jong-June Jeon
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 30,656 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,991 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments and sections like 4.2 Evaluation Metrics and 4.3 Results with tables (e.g., Table 1: Averaged MLu metrics (MARE, F1).). |
| Researcher Affiliation | Academia | Seunghwan An and Jong-June Jeon Department of Statistical Data Science, University of Seoul, S. Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 Discretization of Estimated CDF |
| Open Source Code | Yes | We release the code at https://github.com/an-seunghwan/DistVAE. |
| Open Datasets | Yes | For evaluation, we consider following real tabular datasets: covertype, credit, loan, adult, cabs, and kings (see Appendix A.8 for detailed data descriptions). ... covertype: https://www.kaggle.com/datasets/uciml/forest-cover-type-dataset |
| Dataset Splits | No | Table 8: Description of datasets. #C represents the number of continuous and ordinal variables. #D denotes the number of discrete variables. Dataset Train/Test Split... covertype 45k/5k (Only train/test splits are specified, not validation). |
| Hardware Specification | Yes | We run all experiments using Geforce RTX 3090 GPU |
| Software Dependencies | No | Our experimental codes are all available with pytorch. (PyTorch version is not specified, and no other software dependencies with versions are listed). |
| Experiment Setup | Yes | Table 9: Hyper-parameter settings for tabular dataset experiments. Model epochs batch size learning rate β (or decoder std range) d M ... Dist VAE 100 256 0.001 0.5 2 10 |