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..
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Authors: Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc V Le, Da Huang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on several tabular datasets demonstrate the superiority of Tab NAS over previous reward-shaping methods: it finds better models that obey the constraints. |
| Researcher Affiliation | Collaboration | Chengrun Yang1, Gabriel Bender1, Hanxiao Liu1, Pieter-Jan Kindermans1, Madeleine Udell2, Yifeng Lu1, Quoc V. Le1, Da Huang1 EMAIL, EMAIL, EMAIL 1 Google Research, Brain Team 2 Stanford University |
| Pseudocode | Yes | detailed pseudocode is provided as Algorithm 2 in Appendix B. |
| Open Source Code | Yes | Our implementation can be found at https://github.com/google-research/tabnas. |
| Open Datasets | Yes | The datasets are publicly available. We also provide pseudocode and full details of our hyperparameters to reproduce our results in Table A1 and A2. |
| Dataset Splits | Yes | To avoid overfitting, we split the labelled portion of a dataset into training and validation splits. Weight updates are carried out on the training split; RL updates are performed on the validation split. |
| Hardware Specification | Yes | We ran all experiments using Tensor Flow on a Cloud TPU v2 with 8 cores. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' and briefly references 'Py Torch' but does not specify version numbers for these or any other software dependencies crucial for reproducibility. |
| Experiment Setup | Yes | More details of experiment setup and results in other search spaces can be found in Appendix C and D. |