Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Clustering with Structural Constraints
Authors: Vaggos Chatziafratis, Rad Niazadeh, Moses Charikar
ICML 2018 | Venue PDF | 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. |