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..
Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations
Authors: Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
NeurIPS 2024 | Venue PDF | 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]. |