Finding Relevant Information via a Discrete Fourier Expansion
Authors: Mohsen Heidari, Jithin Sreedharan, Gil I Shamir, Wojciech Szpankowski
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we present an algorithm based on our measure and verify our findings via numerical experiments on various datasets. |
| Researcher Affiliation | Collaboration | 1NSF Center for Science of Information, Purdue University, West Lafayette, USA 2Wadhwani AI, Mumbai, India 3Google Inc., Pittsburgh, USA. |
| Pseudocode | Yes | Procedure 1 FOURIER-ORTH |
| Open Source Code | Yes | The source codes are available at https://github.com/jithin-k-sreedharan/Fourier feature selection. |
| Open Datasets | Yes | The real-world datasets are benchmarks and taken from (Li et al., 2018) and the UCI repository (Dua & Graff, 2017). |
| Dataset Splits | Yes | The experiments employ 5-fold cross-validation with feature selection and the support vector machine (SVM) classifier with radial basis function as a kernel. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions using the support vector machine (SVM) classifier and refers to implementations from external sources (Li et al., 2018) for other algorithms, but no specific software versions (e.g., Python 3.x, PyTorch 1.x, scikit-learn 0.x) are listed. |
| Experiment Setup | No | The paper states that experiments use 'support vector machine (SVM) classifier with radial basis function as a kernel' but does not provide specific hyperparameter values (e.g., learning rate, batch size, SVM parameters C/gamma) or other detailed training configurations. |