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..
A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization
Authors: Frank Curtis
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments illustrate that the method and a limited memory variant of it are stable and outperform (mini-batch) stochastic gradient and other quasi-Newton methods when employed to solve a few machine learning problems. |
| Researcher Affiliation | Academia | Frank E. Curtis EMAIL Department of ISE, Lehigh University, 200 W. Packer Ave., Bethlehem, PA 18015 USA |
| Pseudocode | Yes | Algorithm SC-BFGS : Self-Correcting BFGS |
| Open Source Code | Yes | Code for running SC, SC-s, SC-L, and SC-L-s is publicly available.1 1http://coral.ise.lehigh.edu/frankecurtis/software/ |
| Open Datasets | Yes | a1a from the LIBSVM website2 with (19) using a logistic loss function. ... rcv1(.binary) data from LIBSVM. ... 10-class mnist dataset. ... 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions using a 'training set' and 'testing set' but does not specify explicit training/validation/test dataset splits, percentages, or absolute counts for reproducibility. It does not explicitly refer to a 'validation set' for hyperparameter tuning. |
| Hardware Specification | Yes | All experiments were run using Matlab R2014b on a Macbook Air with a 1.7 GHz Intel Core i7 processor and 8GB of RAM. |
| Software Dependencies | Yes | Algorithms SC-BFGS and SC-L-BFGS were implemented in Matlab... All experiments were run using Matlab R2014b |
| Experiment Setup | Yes | For all algorithms, diminishing stepsize sequences of the form αk = ω0/(ω1 + k) for all k N (20) were tested for all combinations of ω0 {20, 22, 24} and ω1 {20, 22, 24}, and sequences of fixed stepsizes, i.e., αk = ω2 for all k N, (21) were tested for ω2 {2 4, 2 2, 20, 22, 24}. For all SC* algorithms, all combinations of η {2 2, 2 4, 2 6} and θ {20, 22} were tested. For SC*-s, all combinations of ρ {2 2, 2 1} η and τ {21, 22} θ were tested, though the choices ˆkmax = 2 and σ = 0 were fixed. For o BFGS and o LBFGS, following (Schraudolph et al., 2007), the stochastic gradient displacement vectors were computed for the sample Sk in iteration k N as... The values ω3 {2 6, 2 4, 2 2, 20} were tested. For all limited memory methods, m = 5 was used. All stochastic gradient estimates were computed by randomly selecting 64 samples uniformly from the training set. |