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..
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
Authors: Elissa Mhanna, Mohamad Assaad
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, a numerical example validates our theoretical results. |
| Researcher Affiliation | Academia | 1Universit e Paris-Saclay, CNRS, Centrale Sup elec, Laboratoire des signaux et syst emes, 91190, Gif-sur Yvette, France. |
| Pseudocode | Yes | Algorithm 1 The 1P-DSGT-NC Algorithm |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | Yes | We aim to classify m images of two digits taken from the MNIST data set (Le Cun & Cortes, 2005) using logistic regression. |
| Dataset Splits | No | The paper mentions splitting the dataset among agents (e.g., 'm = 12183 images in total and divided equally over n = 31 agents') and an 'independent test set', but it does not explicitly provide details about training, validation, and test splits (e.g., percentages or sample counts for each). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or cloud computing specifications). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The querying noise is ζi,k N(0, 1), i N, the stochastic variable s standard deviation is σu = 0.01, the regularization constant is c = 0.1, the step sizes are ηk = 1.5(k + 1) 0.51 and γk = 3.5(k + 1) 0.17, and every dimension of the perturbation vector zk is chosen from { 1 d} with equal probability. For the DSGT-NC algorithm, the step size is ηk = 2.5(k + 1) 0.51, and no other noise than that on the exact gradient is considered. Both algorithms are initialized with the same random weights vectors θi,0 U([ 1, 1]d), i N, per simulation instance. |