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..
Adaptive Stochastic Alternating Direction Method of Multipliers
Authors: Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Encouraging empirical results on a variety of real-world datasets confirm the effectiveness and efficiency of the proposed algorithms. |
| Researcher Affiliation | Collaboration | Peilin Zhao , EMAIL Jinwei Yang EMAIL Tong Zhang EMAIL Ping Li EMAIL Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Department of Mathematics, Rutgers University; and Department of Mathematics, University of Notre Dame, USA Department of Statistics & Biostatistics, Rutgers University, USA; and Big Data Lab, Baidu Research, China Department of Statistics & Biostatistics, Department of Computer Science, Rutgers University; and Baidu Research, USA |
| Pseudocode | Yes | Algorithm 1 Adaptive Stochastic Alternating Direction Method of Multipliers (Ada-SADMM). Algorithm 2 Adaptive Stochastic ADMM with Diagonal Matrix Update (Ada-SADMMdiag). Algorithm 3 Adaptive Stochastic ADMM with Full Matrix Update (Ada-SADMMfull). |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | To examine the performance, we test all the algorithms on six real-world datasets from web machine learning repositories, which are listed in the Table 1. The news20 dataset was downloaded from www.cs.nyu.edu/ roweis/data.html. All other datasets were downloaded from the LIBSVM website. |
| Dataset Splits | Yes | For each dataset, we randomly divide it into two folds: training set with 80% of examples and test set with the rest. |
| Hardware Specification | No | All experiments were run in Matlab over a machine of 3.4GHz CPU. This description is not specific enough to identify the hardware model (e.g., Intel Core i7, Xeon), family, or number of cores. |
| Software Dependencies | No | All experiments were run in Matlab over a machine of 3.4GHz CPU. This only mentions 'Matlab' without any version number or specific libraries used. |
| Experiment Setup | Yes | In particular, we set the penalty parameter γ = ν = 1/n, where n is the number of training examples, and the trade-off parameter β = 1. In addition, we set the step size parameter ηt = 1/(γt) for SADMM according to the theorem 2 in (Ouyang et al., 2013). Finally, the smooth parameter a is set as 1, and the step size for adaptive stochastic ADMM algorithms are searched from 2[ 5:5] using cross validation. |