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..
Ultrametric Fitting by Gradient Descent
Authors: Giovanni Chierchia, Benjamin Perret
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed cost functions on synthetic and real datasets, and we show that they perform as good as Ward method and semi-supervised SVM. |
| Researcher Affiliation | Academia | Giovanni Chierchia Université Paris-Est, LIGM (UMR 8049) CNRS, ENPC, ESIEE Paris, UPEM F-93162, Noisy-le-Grand, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Solution to the ultrametric fitting problem defined in (4). Algorithm 2 Subdominant ultrametric operator defined in (5) with (14). |
| Open Source Code | Yes | Our code is made publicly available at https://github.com/Perret B/ultrametric-fitting. |
| Open Datasets | Yes | We evaluate the proposed optimization framework on five datasets downloaded from the LIBSVM webpage,3 whose size ranges from 270 to 1500 samples. For each dataset, we build a 5-nearest-neighbor graph, to which we add the edges of a minimum spanning tree to ensure the connectivity. https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions a "10-fold scheme" for cross-validation and that "cross-validated performance is reported," but it does not specify explicit training/validation/test dataset splits where a separate validation set is used for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Higra [44] and Py Torch [45] libraries" as dependencies but does not specify their version numbers. |
| Experiment Setup | Yes | The "Dasgupta" method refers to Algorithm 1 with JDasgupta + λJsize and λ = 1. The "Closest+Size" method refers to Algorithm 1 with the cost function Jclosest + λJsize and λ = 10. In both cases, the regularization is only applied to the top-10 dendogram nodes (see supplemental material). The "Closest+Triplet" method refers to Algorithm 1 with Jclosest + λJtriplet, λ = 1 and α = 10. |