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..
RISE: Robust Individualized Decision Learning with Sensitive Variables
Authors: Xiaoqing Tan, Zhengling Qi, Christopher Seymour, Lu Tang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The reliable performance of the proposed method is demonstrated through synthetic experiments and three real-world applications. |
| Researcher Affiliation | Academia | Xiaoqing Tan University of Pittsburgh EMAIL Qi George Washington University EMAIL W. Seymour University of Pittsburgh EMAIL Tang University of Pittsburgh EMAIL |
| Pseudocode | Yes | Algorithm 1: RISE (Robust individualized decision learning with sensitive variables) |
| Open Source Code | Yes | 1Python code is available at https://github.com/ellenxtan/rise. |
| Open Datasets | Yes | To illustrate the implication of the proposed method from a fairness perspective, we consider the National Supported Work (NSW) program [30] for improving personalized recommendations of a job training program on increasing incomes. ... To illustrate the implication of the proposed method from a safety perspective when there is delayed information, we consider the ACTG175 dataset among HIV positive patients [19]. |
| Dataset Splits | Yes | For simulation, we consider training data and testing data with sample sizes of 8,000 and 2,000, respectively. For real-data applications, we consider a 80-20 split of the dataset into a training data and a testing data. ... The details on modeling and hyperparameter tuning via cross-validations are given in Appendix C. |
| Hardware Specification | Yes | Experiments are performed on a 6-core Intel Xeon CPU E5-2620 v3 2.40GHz equipped with 64GB RAM. |
| Software Dependencies | No | The paper mentions software like 'neural networks' and 'Python package rise' and 'TensorFlow' but does not specify exact version numbers for these components. |
| Experiment Setup | Yes | The details on modeling and hyperparameter tuning via cross-validations are given in Appendix C. |