Hierarchical Clustering with Structural Constraints
Authors: Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results. We run experiments on the Zoo dataset (Lichman, 2013) to demonstrate our approach and the performance of our algorithms for a taxonomy application. |
| Researcher Affiliation | Academia | Department of Computer Science, Stanford University, Stanford, CA, USA. |
| Pseudocode | Yes | Algorithm 1 CRSC |
| Open Source Code | No | The paper mentions "Due to lack of space, we present these results in the full online version of our paper (Chatziafratis et al., 2018). URL https://arxiv.org/abs/1805.09476." This URL points to the paper itself, not to source code. There is no explicit statement about releasing code for the described methodology. |
| Open Datasets | Yes | We run experiments on the Zoo dataset (Lichman, 2013). Lichman, M. Uci machine learning repository, zoo dataset, 2013. URL http://archive.ics.uci.edu/ml/datasets/zoo. |
| Dataset Splits | No | The paper mentions using the "Zoo dataset" but does not provide any specific training, validation, or test split percentages or methodology within the provided text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper describes the theoretical algorithms and their guarantees, and mentions experiments on a dataset, but does not specify experimental setup details such as hyperparameters or training configurations. |