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..
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Zeno outperforms existing approaches.In this section, we evaluate the fault tolerance of the proposed algorithm. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois, Urbana-Champaign, USA. Correspondence to: Cong Xie <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Zeno |
| Open Source Code | Yes | The detailed network architecture can be found in https:// github.com/xcgoner/icml2019_zeno. |
| Open Datasets | Yes | We conduct experiments on benchmark CIFAR-10 image classification dataset (Krizhevsky & Hinton, 2009) |
| Dataset Splits | No | The paper mentions '50k images for training and 10k images for testing' but does not specify a validation set or detailed split percentages or methodology beyond this general training/testing division for reproducibility. |
| Hardware Specification | No | The paper states 'In each experiment, we launch 20 worker processes' but provides no specific details on the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or TensorFlow versions) that are required to replicate the experiments. |
| Experiment Setup | Yes | In all the experiments, we take the learning rate γ = 0.1, worker batch size 100, Zeno batch size nr = 4, and ρ = 0.0005.Each epoch has 25 iterations. |