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..
Adaptive Data Debiasing through Bounded Exploration
Authors: Yifan Yang, Yang Liu, Parinaz Naghizadeh
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of our algorithm using experiments on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Yifan Yang Ohio State University EMAIL Yang Liu University of California, Santa Cruz EMAIL Parinaz Naghizadeh Ohio State University EMAIL |
| Pseudocode | Yes | Our active debiasing algorithm is summarized below. A pseudo-code is given in Appendix C. |
| Open Source Code | Yes | Our code is available at: https://github.com/Yifankevin/adaptive_data_debiasing. |
| Open Datasets | Yes | Adult dataset [12] and the FICO credit score dataset [37] pre-processed by [16]. |
| Dataset Splits | No | We use 2.5% of the data to obtain a biased estimate of the parameter . The remaining data arrives sequentially. |
| Hardware Specification | No | All experiments were run on a local computer. |
| Software Dependencies | No | No specific software versions (e.g., PyTorch 1.9, Python 3.8) are mentioned. |
| Experiment Setup | Yes | Our algorithm sets 1 = 50 and 0 = 60 percentiles, and exploration frequencies t are selected adaptively by both our algorithm and pure exploration. |