Replicable Clustering
Authors: Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose such algorithms for the statistical k-medians, statistical k-means, and statistical k-centers problems by utilizing approximation routines for their combinatorial counterparts in a black-box manner. In addition, we provide experiments on synthetic distributions in 2D using the k-means++ implementation from sklearn as a black-box that validate our theoretical results. |
| Researcher Affiliation | Collaboration | Hossein Esfandiari Google Research esfandiari@google.com Amin Karbasi Yale University, Google Research amin.karbasi@yale.edu Vahab Mirrokni Google Research mirrokni@google.com Grigoris Velegkas Yale University grigoris.velegkas@yale.edu Felix Zhou Yale University felix.zhou@yale.edu |
| Pseudocode | Yes | Algorithm 4.1 Replicable Quad Tree; Algorithm D.1 Replicable k-Means with ε-Cover; Algorithm D.2 Replicable Heavy Hitters; Algorithm D.3 Replicable Coreset; Algorithm D.4 Replicable Rounding; Algorithm F.1 Oracle with Grid; Algorithm F.2 Replicable Active Cells |
| Open Source Code | Yes | https://anonymous.4open.science/r/replicable_clustering_experiments-E380 |
| Open Datasets | No | The paper mentions "synthetic distributions in 2D using the k-means++ implementation from sklearn" and specifically refers to "two moons distribution" and "mixture of truncated Gaussian distributions." However, it does not provide any concrete access information (link, DOI, citation) to these synthetic datasets. |
| Dataset Splits | No | The paper conducts experiments on synthetic data ( |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper mentions using "sklearn" and "k-means++" but does not specify version numbers for these software components. |
| Experiment Setup | No | The paper mentions using "k = 3" for k-means++ in the experiments. No other specific hyperparameters or training configurations are provided for the experimental setup. |