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..
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Authors: Hadrien Hendrikx, Francis Bach, Laurent Massoulié
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate the theoretical results by showing how ADFS compares with MSDA [Scaman et al., 2017], ESDACD [Hendrikx et al., 2019], Point-SAGA [Defazio, 2016], and DSBA [Shen et al., 2018]. All algorithms (except for DSBA, for which we fine-tuned the step-size) were run with out-of-the-box hyperparameters given by theory on data extracted from the standard Higgs, Covtype and RCV1 datasets from Lib SVM. |
| Researcher Affiliation | Academia | Hadrien Hendrikx INRIA DIENS PSL Research University EMAIL Francis Bach INRIA DIENS PSL Research University EMAIL Laurent Massouli e INRIA DIENS PSL Research University EMAIL |
| Pseudocode | Yes | Algorithm 1 ADFS(A, (σi), (Li,j), (µk ), (pk ), ρ) |
| Open Source Code | Yes | A Python implementation of ADFS is also provided in supplementary material. |
| Open Datasets | Yes | data extracted from the standard Higgs, Covtype and RCV1 datasets from Lib SVM. |
| Dataset Splits | No | The paper mentions using standard datasets (Higgs, Covtype, RCV1) but does not provide specific details on how these datasets were split into training, validation, or test sets for their experiments. |
| Hardware Specification | No | Experiments were run in a distributed manner on an actual computing cluster. This does not provide specific hardware models or detailed specifications. |
| Software Dependencies | No | A Python implementation of ADFS is also provided in supplementary material. This mentions Python but no specific version or other software dependencies with version numbers. |
| Experiment Setup | Yes | All algorithms (except for DSBA, for which we fine-tuned the step-size) were run with out-of-the-box hyperparameters given by theory and logistic regression task with m = 104 points per node, regularization parameter σ = 1 and communication delays τ = 5 on 2D grid networks of different sizes. |