Fair Clustering Through Fairlets
Authors: Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the price of fairness by quantifying the value of fair clustering on real-world datasets with sensitive attributes. |
| Researcher Affiliation | Collaboration | Flavio Chierichetti Dipartimento di Informatica Sapienza University Rome, Italy; Ravi Kumar Google Research 1600 Amphitheater Parkway Mountain View, CA 94043 |
| Pseudocode | No | The paper describes algorithms and their steps in prose and mathematical formulations but does not include structured pseudocode blocks or clearly labeled algorithm figures. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for its methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We consider 3 datasets from the UCI repository Lichman (2013) for experimentation. Diabetes. This dataset2 represents the outcomes of patients pertaining to diabetes. ... Bank. This dataset3 contains one record for each phone call in a marketing campaign ran by a Portuguese banking institution (Moro et al. , 2014)). ... Census. This dataset4 contains the census records extracted from the 1994 US census (Kohavi, 1996). |
| Dataset Splits | No | The paper mentions subsampling datasets to a certain number of records but does not provide specific details on how these datasets were split into training, validation, or test sets, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances with specs) used to run the experiments. |
| Software Dependencies | No | The paper mentions specific algorithms used (e.g., Gonzalez (1985), Arya et al. (2004)) but does not list specific software libraries, frameworks, or their version numbers (e.g., Python, PyTorch, scikit-learn versions) required to replicate the experiments. |
| Experiment Setup | No | The paper states that experiments were run with t=2 and varied k, but it does not provide specific hyperparameters (e.g., learning rate, batch size, optimizer settings) or other detailed configuration steps for the experimental setup. |