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].
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. |