Guarantees for Spectral Clustering with Fairness Constraints
Authors: Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a number of experiments. We first study our fair versions of spectral clustering, Algorithm 2 and Algorithm 3, on synthetic data generated according to our variant of the SBM and compare our algorithms to standard SC. We also study how robust our algorithms are with respect to a certain perturbation of our model. We then compare our algorithms to standard SC on real network data. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Rutgers University, NJ 2College of Computing, Georgia Tech, GA. |
| Pseudocode | Yes | Algorithm 1 Unnormalized SC, Algorithm 2 Unnormalized SC with fairness constraints |
| Open Source Code | Yes | The code is available on https://github.com/matthklein/fair-spectral-clustering. |
| Open Datasets | Yes | The first row of Figure 5 shows the results as a function of the number of clusters k for two high school friendship networks (Mastrandrea et al., 2015)... The second row shows the results for DRUGNET, a network encoding acquaintanceship between drug users in Hartford, CT (Weeks et al., 2002). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits for their experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | We implemented all algorithms in MATLAB. We used the built-in function for k-means clustering... (No version numbers provided for MATLAB or specific libraries). |
| Experiment Setup | Yes | We used the built-in function for k-means clustering with all parameters set to their default values except for the number of replicates, which we set to 10. |