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..
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Authors: Jessie X.T. Chen, Miles Lopes
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a collection of experiments that study how well Algorithms 1 and 2 can estimate the errors of NEWTON SKETCH and GIANT in the context of ℓ2-regularized logistic regression. |
| Researcher Affiliation | Academia | 1Department of Mathematics, University of California, Davis 2Department of Statistics, University of California, Davis. |
| Pseudocode | Yes | Algorithm 1 Error estimation for NEWTON SKETCH |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We used the SUSY regression dataset of size (n = 5,000,000, d = 18), which can be obtained from LIBSVM (Chang & Lin, 2011). |
| Dataset Splits | No | The paper uses the SUSY regression dataset but does not explicitly provide train/validation/test splits or mention cross-validation details. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | For all the experiments, the regularization parameter was chosen as γ = 10 3, and the number of bootstrap samples was chosen as B = 12. The step size ηk at each iteration of NEWTON SKETCH and GIANT was determined by the Armijo line search so that f(wk + ηk e k) f(wk) + ηkβ e k, gk . Specifically, the control parameter β was set to β = 0.1, and the search for the step size was restricted to a grid of values ηk {20, 2 1, . . . , 2 10}. with a sketch size of t = n/32. We chose the number of machines to be m = 32 for all datasets |