Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Authors: Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we empirically evaluate our findings. Finally, Section 6 discusses open problems and future directions of research. |
| Researcher Affiliation | Collaboration | Kwang-Sung Jun The University of Arizona kjun@cs.arizona.edu Ashok Cutkosky Google Research ashok@cutkosky.com Francesco Orabona Boston University francesco@orabona.com |
| Pseudocode | Yes | Algorithm 1 KTR3: Kernel Truncated Randomized Ridge Regression |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper describes a synthetic dataset generation process (e.g., "We consider the uniform distribution ρX on X r0, 1s and define the target function to be f pxq Λ β 2 px, 0q for x P X. We define the observed response of x to be f pxq B where B is a uniform random variable r ϵ, ϵs") rather than using or providing access to a publicly available dataset. |
| Dataset Splits | No | The paper mentions drawing "n training points" and estimating excess risk by a "test set" but does not specify the splitting percentages, sample counts, or methodology for creating distinct training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list any software or library names with specific version numbers. |
| Experiment Setup | Yes | For each n in fine-grained grid points in r102, 103s and λ in another fine-grained set of numbers, we draw n training points, compute fn by Algorithm 1, and estimate its excess risk by a test set. Finally, for each n we choose the λ that minimizes the average excess risk. We repeat the same 5 times. First, we set b 1/8 and β 7/16, and ϵ 0.1. [...] To verify our improved rate in the regime 2β b < 1, we also consider the case of β 1/6, and ϵ 0.1. |