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 [1].
A (1+ε)-Approximation for Ultrametric Embedding in Subquadratic Time
Authors: Gabriel Bathie, Guillaume Lagarde
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 1.2 Our Contribution Experimental Results To complement our theoretical results and to demonstrate the practical efficiency of our algorithm, we perform an extensive set of experiments. We measure the performance of our algorithm both in terms of approximation factor and running time on five classical and diverse real-world datasets, and evaluate its scalability on large synthetic datasets. |
| Researcher Affiliation | Academia | Gabriel Bathie1, 2, Guillaume Lagarde1 1 La BRI, Universit e de Bordeaux, France 2 DI ENS, PSL Research University, Paris, France |
| Pseudocode | Yes | Algorithm 1: γ α-approx. of the best ultrametric fit...Algorithm 2: α-approximation of the cut weights |
| Open Source Code | No | Our algorithm is implemented in the Rust programming language, version 1.79.0 (129f3b996, 2024-06-10). Our code was compiled in release mode. The paper mentions the implementation language and version, but does not provide concrete access to the source code (e.g., a repository link or an explicit statement of public availability). |
| Open Datasets | Yes | All datasets are publicly available on the UCI ML Repository (Kelly, Longjohn, and Nottingham 2024b), or Kaggle (Kelly, Longjohn, and Nottingham 2024a) for the DIABETES dataset. |
| Dataset Splits | No | We measure the performance of our algorithm both in terms of approximation factor and running time on five classical and diverse real-world datasets, and evaluate its scalability on large synthetic datasets. The paper mentions using real-world and synthetic datasets for evaluation but does not specify any training, validation, or test splits. The experimental setup describes running the algorithm multiple times on the datasets, not splitting them for model training/evaluation. |
| Hardware Specification | Yes | The hardware configuration includes an Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz and 126GB of RAM. |
| Software Dependencies | Yes | our algorithm is implemented in the Rust programming language, version 1.79.0 (129f3b996, 2024-06-10). |
| Experiment Setup | Yes | We measure the performance of our BUF c-approximation algorithm using our γ-KT and α-ACW algorithms with α = γ = c, which we call FASTULT. ... We evaluate FASTULT for different values of c, and for each value we run the algorithm t = 30 times on each of the 5 datasets, for a total of 150 runs per value of c. |