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..
Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization
Authors: Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, experimental results underscore the empirical advantages of our proposed algorithms when applied to real-world datasets. |
| Researcher Affiliation | Academia | 1School of Information Management and Engineering, Shanghai University of Finance and Economics 2School of Data Science, Fudan University |
| Pseudocode | Yes | Algorithm 1: DGFM at each node i |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology was found. |
| Open Datasets | Yes | Data: We evaluate our proposed algorithms using several standard datasets in LIBSVM (Chang and Lin 2011), which are described in Table 1. |
| Dataset Splits | No | The paper mentions training and testing data but does not explicitly provide details about validation dataset splits (percentages, counts, or explicit standard validation sets). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, cloud instance types) used for running experiments were provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Throughout all the experiments, we set δ = 0.001 and tune the stepsize η from {0.0005, 0.001, 0.005, 0.01} for all four algorithms and b from {10, 100, 500}, T from {10, 50, 100} for DGFM+ and GFM+, T from {1, 5, 10} for DGFM+, m = 20 for two decentralized algorithms. ... Throughout all the experiments, we set δ = 0.01, b = {16, 32, 64}. For DGFM+ and GFM+, we tune b from {40, 80, 800, 1600}, T from {2, 5, 10, 20}. Additionally, tune T from {1, 10, 20} for DGFM+. For all algorithms, we tune the stepsize η from {0.05, 0.1, 0.5, 1} and multiply a decay factor 0.6 if no improvement in 300 iterations. For all experiments, we set the initial perturbation as 0. |