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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Zeno++: Robust Fully Asynchronous SGD
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,377 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,246 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 Byzantine-tolerant asynchronous SGD algorithms. We conduct experiments on two benchmarks: CIFAR-10 image classification dataset (Krizhevsky, 2009), and Wiki Text-2 language modeling dataset (Merity et al., 2017). Our empirical results show good performance compared to previous work. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Illinois, Urbana-Champaign, USA. |
| Pseudocode | Yes | Algorithm 1 Zeno+. Algorithm 2 Zeno++. |
| Open Source Code | No | The detailed network architecture can be found in our submitted source code (will be released upon publication). |
| Open Datasets | Yes | We conduct experiments on two benchmarks: CIFAR-10 image classification dataset (Krizhevsky, 2009), and Wiki Text-2 language modeling dataset (Merity et al., 2017). |
| Dataset Splits | Yes | From the training set, we randomly extracted 2.5k of them as the validation set for Zeno++, the remaining are randomly partitioned onto all the workers. |
| Hardware Specification | No | This work was funded in part by the following grants: NSF IIS 1909577, NSF CNS 1908888, and a JP Morgan Chase Fellowship, along with computational resources donated by Intel, AWS, and Microsoft Azure. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In all the experiments, we take the learning rate γ = 0.1, mini-batch size n = ns = 128, ρ = 0.002, ϵ = 0.1, k = 10. In all the experiments, we take the learning rate γ = 20, mini-batch size n = ns = 20, k = kw = 10, ρ = 10, ϵ = 2. |