Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations
Authors: Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the processing time and accuracy of our method on feature selection tasks. We performed experiments on five datasets from the LIBSVM [9] and Open ML [34]: gisette, robert, ledgar, real-sim, and epsilon. |
| Researcher Affiliation | Industry | 1NTT Computer and Data Science Laboratories 2NTT Communication Science Laboratories |
| Pseudocode | Yes | Algorithm 1 Iterative Hard Thresholding; Algorithm 2 Update of candidate set; Algorithm 3 Update of threshold; Algorithm 4 Fast Iterative Hard Thresholding. |
| Open Source Code | No | The NeurIPS Paper Checklist explicitly states 'Answer: [No]' for the question 'Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?' |
| Open Datasets | Yes | We performed experiments on five datasets from the LIBSVM [9] and Open ML [34]: gisette, robert, ledgar, real-sim, and epsilon. |
| Dataset Splits | No | The paper lists datasets used but does not explicitly provide specific training/validation/test dataset splits (percentages, sample counts, or explicit predefined splits). |
| Hardware Specification | Yes | All the experiments were conducted on a 3.20 GHz Intel CPU with six cores and 64 GB of main memory. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set step sizes of all the methods η = 1/λ where λ is the largest eigen value of X X by following [23]. We stopped these methods when the relative tolerance of the parameter vector dropped below 10 5 [23, 15]. |