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].
Online Learning with an Unknown Fairness Metric
Authors: Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on T, while obtaining an optimal O( T) regret bound to the best fair policy. We prove our theorem in two steps. |
| Researcher Affiliation | Academia | Stephen Gillen University of Pennsylvania EMAIL Christopher Jung Michael Kearns Aaron Roth University of Pennsylvania EMAIL |
| Pseudocode | No | The paper describes algorithms conceptually and refers to existing algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets or data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |