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..
Byzantine Stochastic Gradient Descent
Authors: Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the problem of distributed stochastic optimization in an adversarial setting where, out of m machines which allegedly compute stochastic gradients every iteration, an -fraction are Byzantine, and may behave adversarially. Our main result is a variant of stochastic gradient descent (SGD) which ο¬nds "-approximate minimizers of convex functions in T = e O(" 1"2m + 2) iterations. Further, we provide a lower bound showing that, up to logarithmic factors, our algorithm is information-theoretically optimal both in terms of sample complexity and time complexity. |
| Researcher Affiliation | Collaboration | Dan Alistarh IST Austria EMAIL Zeyuan Allen-Zhu Microsoft Research AI EMAIL Jerry Li Simons Institute EMAIL |
| Pseudocode | Yes | Algorithm 1 Byzantine SGD( , x1, D, T, TA, TB) |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state its release. |
| Open Datasets | No | The paper is theoretical and does not use or mention any specific datasets. |
| Dataset Splits | No | The paper is theoretical and does not mention any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not describe specific experimental setup details such as hyperparameters or training configurations, as it is a theoretical work. |