Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Authors: Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon
ICML 2016 | Conference PDF | Archive PDF | Plain Text | 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 IGOR.COLIN@TELECOM-PARISTECH.FR LTCI, CNRS, T el ecom Paris Tech, Unversit e Paris-Saclay, 75013 Paris, France, Aur elien Bellet AURELIEN.BELLET@INRIA.FR Magnet Team, INRIA Lille Nord Europe, 59650 Villeneuve d Ascq, France, Joseph Salmon JOSEPH.SALMON@TELECOM-PARISTECH.FR LTCI, CNRS, T el ecom Paris Tech, Unversit e Paris-Saclay, 75013 Paris, France, St ephan Cl emenc on STEPHAN.CLEMENCON@TELECOM-PARISTECH.FR 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. |