Subspace Embeddings for the Polynomial Kernel
Authors: Haim Avron, Huy Nguyen, David Woodruff
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 haimav@us.ibm.com; Huy L. Nguy ˆen Simons Institute, UC Berkeley Berkeley, CA 94720 hlnguyen@cs.princeton.edu; David P. Woodruff IBM Almaden Research Center San Jose, CA 95120 dpwoodru@us.ibm.com |
| 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). |