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].
The ensmallen library for flexible numerical optimization
Authors: Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson
JMLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical comparisons show that ensmallen outperforms other frameworks while providing more functionality. The paper presents performance tables (Table 1 and Table 2) comparing runtimes for optimizing linear regression parameters and training logistic regression models on various datasets. |
| Researcher Affiliation | Collaboration | The author affiliations include a mix of industry (Relational AI, Epsilon) and academic/research institutions (Free University of Berlin, Birla Institute of Technology and Science Pilani, University of Texas at Arlington, Data61/CSIRO, and Griffith University). |
| Pseudocode | Yes | Figure 1 provides an "Example implementation of an objective function class for linear regression and usage of the L-BFGS optimizer", which includes structured C++ code demonstrating the methodology. |
| Open Source Code | Yes | The library is available at https://ensmallen.org and is distributed under the permissive BSD license. The source code and documentation are freely available at https://ensmallen.org. |
| Open Datasets | Yes | The paper uses "various real datasets from the UCI dataset repository (Lichman, 2013)", which is a well-known public dataset repository with proper attribution. |
| Dataset Splits | No | The paper mentions using "Highly noisy random data" and datasets from the "UCI dataset repository," but it does not specify any training/test/validation splits, percentages, or methodology for data partitioning needed to reproduce the data partitioning. |
| Hardware Specification | Yes | The experiments were performed on an AMD Ryzen 7 2700X with 64GB RAM, with g++ 10.2.0, Julia 1.5.2, Python 3.8.5, and Octave 6.1.0. |
| Software Dependencies | Yes | The paper specifies software versions: "g++ 10.2.0, Julia 1.5.2, Python 3.8.5, and Octave 6.1.0.". |
| Experiment Setup | Yes | The paper specifies a key experimental setting: "In each framework, the provided L-BFGS optimizer is limited to 10 iterations." |