Estimating the Error of Randomized Newton Methods: A Bootstrap Approach
Authors: Jessie X.T. Chen, Miles Lopes
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 |