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.
Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
Authors: Yijin Ni, Xiaoming Huo
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 24,088 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,728 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 | While our focus is on theoretical development, the results lay essential groundwork for principled and provably fair algorithmic design in future empirical studies. Our current work is theoretical. However, it lays the foundation for future empirical studies. |
| Researcher Affiliation | Academia | Yijin Ni H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology EMAIL Xiaoming Huo H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper provides mathematical formulations and theoretical proofs but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | This paper does not include experiments requiring code. |
| Open Datasets | No | This paper does not include experiments. |
| Dataset Splits | No | This paper does not include experiments, thus no dataset splits are provided. |
| Hardware Specification | No | This paper does not include experiments. |
| Software Dependencies | No | This paper does not include experiments. |
| Experiment Setup | No | This paper does not include experiments. |