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..
Stochastic Block BFGS: Squeezing More Curvature out of Data
Authors: Robert Gower, Donald Goldfarb, Peter Richtarik
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical tests on large-scale logistic regression problems reveal that our method is more robust and substantially outperforms current state-of-the-art methods. |
| Researcher Affiliation | Academia | Robert M. Gower EMAIL Donald Goldfarb EMAIL Peter Richt arik EMAIL |
| Pseudocode | Yes | Algorithm 1 Stochastic Block BFGS Method; Algorithm 2 Block L-BFGS Update (Two-loop Recursion); Algorithm 3 Block L-BFGS Update (Factored loop recursion) |
| Open Source Code | Yes | All the code for the experiments can be downloaded from www.maths.ed.ac.uk/ prichtar/i software.html. |
| Open Datasets | Yes | We tested seven empirical risk minimization problems with a logistic loss and L2 regularizer using data from LIBSVM (Chang & Lin, 2011). |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages, sample counts, or explicit files). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | All the methods were implemented in MATLAB. (Does not specify version number for MATLAB or other dependencies). |
| Experiment Setup | Yes | We set the regularization parameter ฮป = 1/n for all experiments. We set the subsampling size |St| = n throughout our tests. We tested each method with a stepsize ฮท {100, 5 ยท 10โ1, 10โ1, . . . , 10โ6, 5 ยท 10โ7, 10โ7} for the best outcome, and used the resulting ฮท. Finally, we used m = n/|St| for the number of inner iterations... We set the memory to 10 for the MNJ method in all tests... |