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..
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Authors: Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
NeurIPS 2019 | Venue PDF | 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 EMAIL Ashok Cutkosky Google Research EMAIL Francesco Orabona Boston University EMAIL |
| 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. |