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..
Polynomial Preconditioning for Gradient Methods
Authors: Nikita Doikov, Anton Rodomanov
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments confirm the efficiency of our preconditioning strategies for solving various machine learning problems. |
| Researcher Affiliation | Academia | Nikita Doikov 1 Anton Rodomanov 2 1EPFL, Switzerland 2UCLouvain, Belgium. Correspondence to: Nikita Doikov <EMAIL>, Anton Rodomanov <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Preconditioned Basic Gradient Method |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in the paper was found. |
| Open Datasets | Yes | Figure 2: Leading eigenvalues (in the logarithmic scale) of the curvature matrix B, for several typical datasets2. 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | Yes | Clock time was evaluated using the machine with Intel Core i5 CPU, 1.6GHz; 8 GB RAM. All methods were implemented in Python. |
| Software Dependencies | No | The paper states 'All methods were implemented in Python.' but does not provide specific version numbers for Python or any libraries used. |
| Experiment Setup | No | The paper discusses parameters and adaptive search procedures but does not provide specific numerical values for hyperparameters or other concrete training configurations for the experiments shown. |