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..
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
Authors: Ron Dorfman, Naseem Amin Yehya, Kfir Yehuda Levy
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 6, we explore the practical aspects and benefits of our approach through experiments on image classification tasks with two dynamic identity-switching strategies. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Technion, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1 Byzantine-Robust Optimization with MLMC |
| Open Source Code | No | The paper does not contain any statement or link providing concrete access to source code for the methodology described. |
| Open Datasets | Yes | We study image classification on the MNIST (Le Cun et al., 1998) and CIFAR-10 (Krizhevsky et al., 2009) datasets |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR-10 datasets and reports test accuracy, but it does not explicitly provide the training, validation, and test dataset splits or their sizes. |
| Hardware Specification | Yes | We run all experiments on a machine with a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions using CNN architectures and training details but does not provide specific version numbers for any software components or libraries like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Additional training details are deferred to Appendix J for brevity. ... Table 2. Training details and hyperparameters. ... Learning rate 10 drop after 4000 iterations 10 drop after 6000 iterations Weight decay 10 4 Base batch size 32 64 |