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-Tolerant Methods for Distributed Variational Inequalities
Authors: Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takac, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our work makes a further step in this direction by providing several (provably) Byzantine-robust methods for distributed variational inequality, thoroughly studying their theoretical convergence, removing the limitations of the previous work, and providing numerical comparisons supporting the theoretical findings. |
| Researcher Affiliation | Academia | Nazarii Tupitsa MBZUAI, MIPT Abdulla Jasem Almansoori MBZUAI Yanlin Wu MBZUAI Martin Takáˇc MBZUAI Karthik Nandakumar MBZUAI Samuel Horváth MBZUAI Eduard Gorbunov |
| Pseudocode | Yes | Algorithm 1 SGDA-RA; Algorithm 2 SEG-RA; Algorithm 3 M-SGDA-RA; Algorithm 4 Check Computations; Algorithm 5 SGDA-CC; Algorithm 6 R-SGDA-CC; Algorithm 7 SEG-CC; Algorithm 8 R-SEG-CC. |
| Open Source Code | Yes | Code for quadratic games is available at https://github.com/nazya/sgda-ra7. ... Code for GANs is available at https://github.com/zeligism/vi-robust-agg. |
| Open Datasets | Yes | We conduct numerical experiments on a quadratic game... Robust Neural Networks training. ... {(xi, yi)}N 1 is the MNIST dataset. ... The dataset we chose for this experiment is CIFAR-10. |
| Dataset Splits | Yes | Specifically, we show the validation error on MNIST after each epoch. |
| Hardware Specification | No | The paper mentions "simulate n = 20 nodes on a single machine" but does not provide specific hardware details such as CPU/GPU models, processor types, or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | We set the parameter α = 0.1 for M-SGDA-RA, and the following parameters for RDEG: αRDEG = 0.06, δRDEG = 0.9 and theoretical value of ϵ. ... γ = 2e 5. ... We fix the learning rate to 0.01 and use a batch size of 32. We run the algorithm for 50 epochs and average our results across 3 runs. ... We let n = 20, B = 4, λ1 = 0, and λ2 = 100. ... We let n = 10, B = 2, and choose a learning rate of 0.001, β1 = 0.5, and β2 = 0.9 with a batch size of 64. We run the algorithms for 4600 epochs. |