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..
Probably Approximately Metric-Fair Learning
Authors: Gal Yona, Guy Rothblum
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a relaxed approximate metric-fairness framework for machine learning, where fairness does generalize from the sample to the underlying population, and present polynomial-time fair learning algorithms in this framework. We proceed to describe our setting and contributions. |
| Researcher Affiliation | Academia | 1Weizmann Institute of Science, Rehovot, Israel. |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code. |
| Open Datasets | No | The paper is theoretical and discusses 'a training set of labeled examples, drawn i.i.d. from a distribution D' in a conceptual manner, but it does not specify any particular public dataset or provide access information for any data used for training. |
| Dataset Splits | No | The paper does not explicitly mention or describe a validation dataset split. It focuses on theoretical generalization from a training set to an underlying distribution. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, thus it does not provide details on specific experimental setup, hyperparameters, or training configurations. |