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..
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Authors: Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical simulations on Area Under the ROC Curve (AUC) maximization and metric learning problems which illustrate the practical interest of our approach. |
| Researcher Affiliation | Academia | Igor Colin EMAIL LTCI, CNRS, T el ecom Paris Tech, Unversit e Paris-Saclay, 75013 Paris, France, Aur elien Bellet EMAIL Magnet Team, INRIA Lille Nord Europe, 59650 Villeneuve d Ascq, France, Joseph Salmon EMAIL LTCI, CNRS, T el ecom Paris Tech, Unversit e Paris-Saclay, 75013 Paris, France, St ephan Cl emenc on EMAIL LTCI, CNRS, T el ecom Paris Tech, Unversit e Paris-Saclay, 75013 Paris, France |
| Pseudocode | Yes | Algorithm 1 Stochastic dual averaging in the centralized setting, Algorithm 2 Gossip dual averaging for pairwise function in synchronous setting, Algorithm 3 Gossip dual averaging for pairwise function in asynchronous setting |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use the Breast Cancer Wisconsin dataset, which consists of n = 699 points in d = 11 dimensions. |
| Dataset Splits | No | The paper mentions datasets used but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names or solver versions, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We initialize each θi to 0 and for each network, we run 50 times Algorithms 2 and 3 with γ(t) = 1/√t. |