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..
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Authors: Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide empirical results on a real dataset to support our theoretical results. We perform experiments on the MNIST dataset that consists of 70K samples, partitioned into a training, tuning, and a test set of sizes 20K, 10K, and 40K, respectively. |
| Researcher Affiliation | Academia | Department of Computer Science, Johns Hopkins University, Baltimore, MD 21204 |
| Pseudocode | Yes | Algorithm 1 KPCA with Random Features (Meta Algorithm) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of their source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We perform experiments on the MNIST dataset that consists of 70K samples, partitioned into a training, tuning, and a test set of sizes 20K, 10K, and 40K, respectively. |
| Dataset Splits | Yes | We perform experiments on the MNIST dataset that consists of 70K samples, partitioned into a training, tuning, and a test set of sizes 20K, 10K, and 40K, respectively. |
| Hardware Specification | No | The paper states that 'Runtime is recorded in a controlled environment; each run executed on identical unloaded compute node.' but does not specify any hardware details such as CPU, GPU models, or memory. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In particular, we choose the RBF kernel k(x, x )=exp x x 2/2ฯ2 with bandwidth parameter ฯ2 = 50. Number of random features and the size of Nystrรถm approximation are set to 750 and 100, respectively. |