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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |