Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Learning with an Unknown Fairness Metric
Authors: Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,084 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,175 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |