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..
On Efficient Low Distortion Ultrametric Embedding
Authors: Vincent Cohen-Addad, Karthik C. S., Guillaume Lagarde
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results We implemented our algorithm and performed experiments on three classic datasets (DIABETES, MICE, PENDIGITS) [...] Finally, we present empirical evaluation on classic machine learning datasets and show that the output of our algorithm is comparable to the output of the linkage algorithms while achieving a much faster running time. |
| Researcher Affiliation | Academia | 1Sorbonne Universit e, UPMC Univ Paris 06, CNRS, LIP6, Paris, France 2Department of Computer Science, Tel Aviv University, Israel 3La BRI, Universit e de Bordeaux, Bordeaux, France. |
| Pseudocode | No | The paper describes algorithms but does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper states 'We implemented our algorithm' but does not provide any explicit statement about releasing the source code or a link to a repository. |
| Open Datasets | Yes | We present some experiments performed on three standard datasets: DIABETES (768 samples, 8 features), MICE (1080 samples, 77 features), PENDIGITS (10992 samples, 16 features) |
| Dataset Splits | No | The paper mentions using "classic datasets" and evaluating performance, but it does not specify any training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using the 'Scikit-learn library' and a 'C++ implementation' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For a parameter γ fixed to 2.5, our results are as follows. |