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.
Individual Fairness in Hindsight
Authors: Swati Gupta, Vijay Kamble
JMLR 2021 | Venue PDF | LLM Run Details | Input Tokens: 30,265 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,750 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 technical contribution is to study the implications of these fairness constraints in situations where an algorithm operates under partial information and needs to learn good decisions over time. Formally, we consider a general online decision-making problem under uncertainty that falls into the class of stochastic contextual bandit problems... Our sublinear upper bounds on the regret of the resulting algorithm are accompanied by matching lower bounds that justify our design. |
| Researcher Affiliation | Academia | Swati Gupta EMAIL School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA; Vijay Kamble EMAIL Department of Information and Decision Sciences University of Illinois at Chicago Chicago, IL 60607, USA |
| Pseudocode | Yes | Algorithm 1: Cautious Fair Exploration (Ca FE) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code, nor does it include a link to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments using specific datasets, therefore no public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments using specific datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe running experiments, therefore no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental runs, therefore no specific experimental setup details or hyperparameter values are provided. |