Coreset for Line-Sets Clustering
Authors: Sagi Lotan, Ernesto Evgeniy Sanches Shayda, Dan Feldman
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our coreset construction algorithms for both Colored-sets and Line-sets. In this section we test there empirical performance on synthetic and real data. Open source code and experiments are also provided. |
| Researcher Affiliation | Academia | Sagi Lotan sagi.lotan@gmail.com Ernesto Evgeniy Sanches Shayda ernestosanches@gmail.com Dan Feldman dannyf.post@gmail.com Robotics & Big Data Labs, Computer Science Department, University of Haifa |
| Pseudocode | Yes | Algorithm 1: CS-DENSE(P, k), Algorithm 2: GROUPED-SENSITIVITY(L, B, k), Algorithm 3: LS-DENSE(L, k), Algorithm 4: CORESET(L, k, η) |
| Open Source Code | Yes | Open source code and experiments are also provided. Open source is available in [32]. [32] S. Lotan, E. E. S. Shayda, and D. Feldman. Coreset for lines sets clustering open source code. https://github.com/ernestosanches/Sets-of-lines-clustering, 2022. |
| Open Datasets | Yes | We used California housing prices data-set [37] in witch we introduced uncertainty by removing two of the 9 dimensions each point. [37] R. K. Pace and R. Barry. California housing dataset. https://scikit-learn.org/stable/ modules/generated/sklearn.datasets.fetch_california_housing.html, 1997. The Reuters-21578 data-set from [30], which results in sets of points corresponding to each paragraph in each document. [30] D. Lewis. Reuters-21578 text categorization test collection, distribution 1.0. http: // www. research. att. com , 1997. |
| Dataset Splits | No | The paper describes the datasets used for experiments but does not provide specific details on train/validation/test splits, such as percentages or sample counts, nor does it refer to standard splits with citations. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware specifications (e.g., GPU/CPU models, memory) used for conducting the experiments. |
| Software Dependencies | No | The paper states 'We implemented our coreset construction algorithms' and mentions open-source code availability, but it does not specify any software dependencies or library versions within the text itself. |
| Experiment Setup | No | The paper describes the general experimental procedure, including varying 'm' and 'k' and generating queries, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, epochs) or training configurations. |