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..
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
Authors: Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Numerical Experiments of De-biased SSN Figure 2 assesses the impact of the sketch size m on both relative error ( ˇβT β 2 H/ ˇβ0 β 2 H) and running time of de-biased SSN employing approximate ridge leverage score sampling (SSN-ARLev), in comparison to the Newton LESS method (Derezi nski et al., 2021a) based on random projection. The results in Figure 2 demonstrate that the proposed de-biased SSN consistently outperforms Newton LESS across all tested sketch size m, exhibiting a superior convergence complexity trade-off. |
| Researcher Affiliation | Academia | 1School of Electronic Information & Communications, Huazhong University of Science & Technology, Wuhan, Hubei, China. 2ICSI, LBNL, and Department of Statistics, University of California, Berkeley, USA. Correspondence to: Zhenyu Liao <EMAIL>. |
| Pseudocode | No | The paper describes steps and methods in regular paragraph text and mathematical derivations. There are no explicitly labeled sections such as 'Algorithm' or 'Pseudocode' nor any structured, code-like formatted procedures. |
| Open Source Code | No | The paper states: 'Further implementation details, including those for the SRHT, are available in the public repository provided by Derezi nski et al. (2021a) at https://github.com/lessketching/newtonsketch.' However, this link refers to a third-party tool ('Newton-LESS') that the authors used as a baseline, not their own implementation code for the novel methodology presented in this paper. |
| Open Datasets | Yes | We solve the following logistic regression problem... sampled from both MNIST (Le Cun et al., 1998) and CIFAR-10 (Krizhevsky, 2009) datasets |
| Dataset Splits | No | The paper states: 'For MNIST data matrix, we have n = 213 and d = 27, and for CIFAR-10 data, we have n = 214 and d = 28.' It describes dataset sizes and a binary classification setup, but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions 'wall-clock time' in its numerical experiments but does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'torchvision.transforms from Py Torch' for pre-processing images, but it does not specify the version number for PyTorch or any other software components. |
| Experiment Setup | Yes | And we fix in Figures 2 and 3 the ridge regularization parameter to λ = 10 2 for both MNIST and CIFAR-10 data in Section 5." and "In our experiments, the first-order methods, Gradient Descent and Stochastic Gradient Descent, are used with a fixed step size. ... second-order methods... employ step sizes that are dynamically adjusted using a line search algorithm based on the Armijo condition (Bonnans et al., 2006, Chapter 3)." |