Randomized Block Cubic Newton Method
Authors: Nikita Doikov, Peter Richtarik, University Edinburgh
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish O(1/ϵ), O(1/ϵ) and O(log(1/ϵ)) rates under different assumptions on the component functions. Lastly, we show numerically that our method outperforms the state of the art on a variety of machine learning problems, including cubically regularized leastsquares, logistic regression with constraints, and Poisson regression. Finally, our numerical experiments on synthetic and real datasets are described in Section 8. |
| Researcher Affiliation | Academia | 1National Research University Higher School of Economics, Samsung-HSE Laboratory, Moscow, Russia 2King Abdullah University of Science and Technology, Thuwal, Saudi Arabia 3University of Edinburgh, Edinburgh, United Kingdom 4Moscow Institute of Physics and Technology, Dolgoprudny, Russia. |
| Pseudocode | Yes | Algorithm 1 RBCN: Randomized Block Cubic Newton; Algorithm 2 Stochastic Dual Cubic Newton Ascent (SDCNA) |
| Open Source Code | No | The paper does not provide an unambiguous statement or a direct link to the open-source code for the methodology described. |
| Open Datasets | No | The paper mentions datasets like "leukemia" and "duke breast-cancer" but does not provide concrete access information (link, DOI, repository name, or formal citation with authors/year) for them to be considered publicly available according to the strict criteria. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments, such as GPU/CPU models, memory, or detailed computer specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions needed to replicate the experiment. |
| Experiment Setup | Yes | Using middle-size blocks of coordinates on each step is the best choice in terms of total computational time; We see that using coordinate blocks of size 25 50 for the Cubic Newton outperforms all other cases of both methods in terms of total computational time; Comparison of Algorithm 2 (marked as Cubic) with SDNA and SDCA methods for minibatch sizes τ = 8, 32, 256, training Poisson regression. |