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..
Optimal Shrinkage for Distributed Second-Order Optimization
Authors: Fangzhao Zhang, Mert Pilanci
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach leads to significant improvements in convergence rate compared to standard baselines and recent proposals, as shown through experiments on both real and synthetic datasets. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Stanford University. |
| Pseudocode | Yes | Algorithm 1 Distributed Newton s method with optimal shrinkage Algorithm 2 Distributed preconditioned conjugate gradient with optimal shrinkage |
| Open Source Code | Yes | Code for experiments is included in the submission. |
| Open Datasets | Yes | All real datasets used in this section are public and available at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | The paper states that data is 'split evenly to each agent' or 'experiment with ten random permutations' but does not specify explicit train/validation/test splits with percentages or sample counts. |
| Hardware Specification | Yes | We run all experiments on google cloud n1-standard-8 machine. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers, such as programming languages or libraries, to ensure reproducibility. |
| Experiment Setup | Yes | We pick m = 5, λ = 0.01,max iters= 20 for heart, m = 2, λ = 0.01,max iters= 10 for liver-disorders, m = 3, λ = 0.1,max iters= 5 for splice, m = 10, λ = 0.1,max iters= 20 for svmguide3, m = 100, λ = 1e 5,max iters= 50 for cod-rna, m = 200, λ = 1e 5,max iters= 50 for covtype, m = 40, λ = 0.01,max iters= 50 for phishing, m = 50, λ = 0.1,max iters= 50 for w8a. |