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..
Subspace Embeddings for the Polynomial Kernel
Authors: Haim Avron, Huy Nguyen, David Woodruff
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report two sets of experiments whose goal is to demonstrate that the k-Space algorithm (Algorithm 1) is useful as a feature extraction algorithm. We use standard classification and regression datasets. ... The results are reported in Table 1. ... The results are reported in Table 2. |
| Researcher Affiliation | Collaboration | Haim Avron IBM T.J. Watson Research Center Yorktown Heights, NY 10598 EMAIL; Huy L. Nguy ˆen Simons Institute, UC Berkeley Berkeley, CA 94720 EMAIL; David P. Woodruff IBM Almaden Research Center San Jose, CA 95120 EMAIL |
| Pseudocode | Yes | Algorithm 1 k-Space |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | No | The paper lists well-known datasets (MNIST, CPU, ADULT, CENSUS, USPS) used in experiments, but does not provide specific access information like a URL, DOI, repository, or a direct citation with author and year for each dataset's source. |
| Dataset Splits | No | The paper specifies 'n' for training instances and 'nt' for test instances in Tables 1 and 2, and mentions 'ns samples from the training set are used', but it does not provide explicit training/validation/test splits with percentages, absolute counts, or a detailed splitting methodology for general reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | In k-Space we use m = O(k) and r = O(k) with the ratio between m and k and r and k as detailed in the table. ... λ = 0.001 (from Table 1 for CENSUS dataset). |