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..
Growing Adaptive Multi-hyperplane Machines
Authors: Nemanja Djuric, Zhuang Wang, Slobodan Vucetic
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments on data of varying characteristics to measure accuracy of the proposed method, and to estimate its robustness to noise. We evaluated the algorithms on 5 real-world data sets of very different sizes, dimensions, and complexities. |
| Researcher Affiliation | Collaboration | Nemanja Djuric 1 Zhuang Wang 2 Slobodan Vucetic 3 1Uber ATG, Pittsburgh, PA, USA 2Facebook, Menlo Park, CA, USA 3Temple University, Philadelphia, PA, USA. |
| Pseudocode | Yes | Algorithm 1 Training algorithm for GAMM |
| Open Source Code | Yes | The GAMM implementation is available for download at https://github.com/djurikom/Budgeted SVM. |
| Open Datasets | Yes | We evaluated the algorithms on 5 real-world data sets of very different sizes, dimensions, and complexities. The datasets include usps, letter, ijcnn1, rcv1, and mnist. A footnote links to their source: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/, last accessed June 2020. |
| Dataset Splits | No | The paper mentions 'We created 15,000 training and 5,000 test examples' for synthetic data and 'λ through cross-validation' for parameter tuning, but does not explicitly state a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | Yes | To better illustrate scalability we evaluated the algorithms on the lower-end Intel R E7400 with 2.80GHz processor and 4GB RAM |
| Software Dependencies | No | The paper mentions using 'scikit-learn implementations' for some baseline models, but does not provide specific version numbers for scikit-learn or any other software dependencies. It mentions C++ as the implementation language for GAMM, and names other tools/libraries without versions. |
| Experiment Setup | Yes | We set parameters to their default values, c = 10 for AMM and c = 50 for GAMM (due to more frequent introduction of new weights), p = 0.2, β = 0.99, set λ through cross-validation, and trained the models for 15 epochs. |