Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,674 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,044 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |