An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
Authors: Hadrien Hendrikx, Francis Bach, Laurent Massoulié
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 hadrien.hendrikx@inria.fr Francis Bach INRIA DIENS PSL Research University francis.bach@inria.fr Laurent Massouli e INRIA DIENS PSL Research University laurent.massoulie@inria.fr |
| 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. |