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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
Authors: David Woodruff, Amir Zandieh
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,970 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,694 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we empirically show that in large-scale regression tasks, our algorithm outperforms state-of-the-art kernel approximation methods. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, USA 2Ecole polytechnique federale de Lausanne, Switzerland. |
| Pseudocode | Yes | Algorithm 1 RECURSIVE LEVERAGE SCORE SAMPLING and Algorithm 2 ROWSAMPLER FOR POLYNOMIAL KERNEL are provided. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We base our comparison on the four standard large-scale regression datasets evaluated in (Le et al., 2013). The size of the data points is denoted by n and the dimensionality is denoted by d in Table 1. DATASET: WINE INSURANCE CT LOCATION FOREST |
| Dataset Splits | Yes | We use the same hyperparameters (kernel bandwidth and regularization parameter) across all kernel approximation methods which were selected via cross-validation on the Fourier features method, as our base-line method. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | We use the same hyperparameters (kernel bandwidth and regularization parameter) across all kernel approximation methods which were selected via cross-validation on the Fourier features method, as our base-line method. |