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].
High dimensional, tabular deep learning with an auxiliary knowledge graph
Authors: Camilo Ruiz, Hongyu Ren, Kexin Huang, Jure Leskovec
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Across 6 d n datasets, PLATO outperforms 13 state-of-the-art baselines by up to 10.19%. We evaluate PLATO against 13 baselines on 10 tabular datasets (6 with d n, 4 with d n). |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University 2Department of Bioengineering, Stanford University |
| Pseudocode | Yes | PLATO is outlined in Algorithm 1. |
| Open Source Code | Yes | Code, data, and the KG are available at https://github.com/snap-stanford/plato. |
| Open Datasets | Yes | We use 6 tabular d n datasets, 4 d n datasets [16, 17, 30, 79], and a KG from prior studies [44, 35, 38, 56, 63, 74, 75] (Appendix G, H)... Data is available at https://github.com/snap-stanford/plato. |
| Dataset Splits | Yes | We split data with a 60/20/20 training, validation, test split. |
| Hardware Specification | Yes | Each model is run on a Ge Force RTX 2080 TI GPU. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with version numbers (e.g., Python, specific libraries like PyTorch or TensorFlow with their versions). |
| Experiment Setup | Yes | We conduct a random search with 500 configurations of every model (including PLATO) on every dataset across a broad range of hyperparameters (Appendix A). Hyperparameter ranges for PLATO are given in Table 7. Hyperparameter ranges for baseline methods are given in Table 8. |