Demographic Parity Constrained Minimax Optimal Regression under Linear Model
Authors: Kazuto Fukuchi, Jun Sakuma
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |