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 Adaptive Quasi-Newton Methods for Minimizing Expected Values
Authors: Chaoxu Zhou, Wenbo Gao, Donald Goldfarb
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared several implementations of SA-GD and SA-BFGS against the original SGD method and the robust SGD method (Nemirovski et al., 2009) on a penalized least squares problem with random design. ... Figure 1 shows the performance of each algorithm on a series of problems with varying problem size p and parameter ",. |
| Researcher Affiliation | Academia | 1Dept. of Industrial Engineering and Operations Research, Columbia University. |
| Pseudocode | Yes | Algorithm 1 SA-GD |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The objective function is based on a 'penalized least squares problem with random design' where 'X was drawn according to a multivariate N(0, \Sigma(\rho))'. This describes a synthetically generated dataset rather than a publicly available one with concrete access information. |
| Dataset Splits | No | The paper defines problem sizes (p=100 and p=500) and describes the generation of random data for a penalized least squares problem. However, it does not specify any explicit train/validation/test dataset splits (e.g., percentages or counts) or reference standard splits from a benchmark dataset. |
| Hardware Specification | Yes | The algorithms were implemented in Matlab 2015a, and the system was an Intel i5-5200U running Ubuntu. |
| Software Dependencies | Yes | The algorithms were implemented in Matlab 2015a |
| Experiment Setup | Yes | SGD: SGD with fixed mk = p and diminishing step sizes tk = 1 k+1000 for problems with p = 100, and tk = 1 k+5000 for p = 500. |