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..
Demographic Parity Constrained Minimax Optimal Regression under Linear Model
Authors: Kazuto Fukuchi, Jun Sakuma
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our analysis reveals that the minimax optimal error for the demographic parity-constrained regression problem under our model is characterized by Θ(d M/n), where n denotes the sample size, d represents the dimensionality, and M signifies the number of demographic groups arising from sensitive attributes. Moreover, we demonstrate that the minimax error increases in conjunction with a larger bias present in the model. |
| Researcher Affiliation | Academia | Kazuto Fukuchi1,3 Jun Sakuma2,3 1 University of Tsukuba 2 Tokyo Institute of Technology 3 RIKEN |
| Pseudocode | Yes | Algorithm 1: Algorithm of the proposed optimal estimator. |
| Open Source Code | No | The paper does not include any statements about making its source code available or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using specific datasets, thus no information about public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with data, so no training/test/validation splits are mentioned. |
| Hardware Specification | No | The paper describes theoretical analysis and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical derivations and proofs, thus it does not describe an experimental setup with hyperparameters or training configurations. |