A Fair Classifier Using Kernel Density Estimation
Authors: Jaewoong Cho, Gyeongjo Hwang, Changho Suh
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that our algorithm achieves greater or comparable performances against prior fair classifers in accuracy-fairness tradeoff as well as in training stability on both synthetic and benchmark real datasets.Our extensive experiments conducted both on synthetic and benchmark real datasets (Law School Admissions [36], Adult Census [6], Credit Card Default [6, 39], and COMPAS [2]) demonstrate that our algorithm achieves higher accuracy-fairness tradeoff relative to the states of the arts [42, 41, 44, 1, 25, 12], both w.r.t. demographic parity and equalized odds. |
| Researcher Affiliation | Academia | Jaewoong Cho EE, KAIST cjw2525@kaist.ac.kr Gyeongjo Hwang EE, KAIST hkj4276@kaist.ac.kr Changho Suh EE, KAIST chsuh@kaist.ac.kr |
| Pseudocode | No | The paper describes the proposed approach and its components using mathematical equations and textual descriptions, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We implement our algorithm in Py Torch [26]' but does not provide any link or explicit statement about releasing the source code for their methodology. |
| Open Datasets | Yes | We provide experimental results conducted on synthetic and four benchmark real datasets (COMPAS [2], Adult Census [6], Law School Admissions [36], and Credit Card Default [6, 39]). |
| Dataset Splits | No | The paper describes train and test splits for the datasets (e.g., "80% train set... and 20% test set" for synthetic, and specific train/test example counts for real datasets like COMPAS, Adult Census, Law School Admissions, and Credit Card Default), but does not explicitly mention a validation split. |
| Hardware Specification | Yes | We implement our algorithm in Py Torch [26], and all experiments are performed on a server with Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions "We implement our algorithm in Py Torch [26]" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We train fair classifiers with a 2-layer NN with 16 hidden nodes. For our approach, we set hyperparameters δ (of the Huber function) and h to be 1 and 0.1, respectively. ... We use the batch size of 512. We use Adam optimizer and its default parameters (β1, β2) = (0.9, 0.999) with the learning rate of 10 2. |