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..
Task-Agnostic Machine-Learning-Assisted Inference
Authors: Jiacheng Miao, Qiongshi Lu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we showcase our method s validity, versatility, and superiority compared to existing approaches. |
| Researcher Affiliation | Academia | Jiacheng Miao University of Wisconsin-Madison EMAIL Qiongshi Lu University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 PSPS for ML-assisted inference |
| Open Source Code | Yes | Our software is available at https://github.com/qlu-lab/psps. |
| Open Datasets | Yes | We used data from the UK Biobank [13], which includes 36,971 labeled and 319,548 unlabeled samples with 9,450,880 genetic variants after quality control. |
| Dataset Splits | Yes | Prediction in the labeled sample was implemented through cross-validation to avoid overfitting. The implementation detail is deferred to Appendix D. We select the predictive variables and train the Soft Impute model using 90% of the labeled data. We then perform predictions on the remaining 10% in each fold and repeat this process 10 times across all folds. |
| Hardware Specification | Yes | All our simulation is run in R with version 4.2.1 (2022-06-23) in a Mac Book Air with an M1 chip. |
| Software Dependencies | Yes | All our simulation is run in R with version 4.2.1 (2022-06-23) in a Mac Book Air with an M1 chip. |
| Experiment Setup | Yes | A pre-trained random forest with 500 trees to grow is obtained from hold-out data. We bootstrap the labeled data for 200 times for covariance estimation. All simulations are repeated 1000 times. |