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..
Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction
Authors: Kenta Niwa, Guoqiang Zhang, W. Bastiaan Kleijn, Noboru Harada, Hiroshi Sawada, Akinori Fujino
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the constructed algorithms by investigating the learning curves for the case that statistically heterogeneous data subsets are placed at the local nodes. |
| Researcher Affiliation | Collaboration | 1NTT Communication Science Laboratories, Kyoto, Japan 2NTT Media Intelligence Laboratories, Tokyo, Japan 3University of Technology Sydney, Sydney, Australia 4Victoria University of Wellington, Wellington, New Zealand. |
| Pseudocode | Yes | Algorithm 1 Previous ECL (Niwa et al., 2020) and Algorithm 2 Proposed ECL-ISVR |
| Open Source Code | Yes | A part of our source code5 is available. 5https://github.com/nttcslab/ecl-isvr |
| Open Datasets | Yes | Fashion MNIST (Xiao et al., 2017) consists of 28 28 pixel of gray-scale images in 10 classes. The CIFAR-10 data set consists of 32 32 color images in 10 object classes (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper mentions training and test data but does not explicitly specify a validation split or how it was used for model selection/tuning. |
| Hardware Specification | Yes | We constructed software that runs on a server that has 8 GPUs (NVIDIA Ge Force RTX 2080Ti) with 2 CPUs (Intel Xeon Gold 5222, 3.80 GHz). |
| Software Dependencies | Yes | Py Torch (v1.6.0) with CUDA (v10.2) and Gloo4 for node communication was used. |
| Experiment Setup | Yes | squired L2 model normalization with weight 0.01 is added to the cost function. The step-size µ = 0.002 and the mini-batch size 100 are used in all settings. The communication for each edge is conducted once per K=8 local updates on average, with R=8, 800 rounds for (N1) and R=5, 600 rounds for (N2). |