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 [1].
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. |