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..
LASER: Linear Compression in Wireless Distributed Optimization
Authors: Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael Gastpar
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the superiority of LASER over the baselines on the challenging tasks of (i) language modeling with GPT-2 WIKITEXT-103 and (ii, iii, iv) image classification on MNIST, CIFAR10 and CIFAR100. With high gradient compression (165 ), LASER achieves 5064% perplexity improvement in the low and moderate power regimes on WIKITEXT-103. |
| Researcher Affiliation | Academia | Ashok Vardhan Makkuva * 1 Marco Bondaschi * 1 Thijs Vogels 1 Martin Jaggi 1 Hyeji Kim 2 Michael Gastpar 1... 1School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland 2Department of Electrical and Computer Engineering, UT Austin, Austin, TX, USA. |
| Pseudocode | Yes | Algorithm 1 LASER |
| Open Source Code | Yes | Code is available at https: //github.com/Bond1995/LASER. |
| Open Datasets | Yes | We empirically demonstrate the superiority of LASER over the baselines on the challenging tasks of (i) language modeling with GPT-2 WIKITEXT-103 and (ii, iii, iv) image classification on MNIST, CIFAR10 and CIFAR100. |
| Dataset Splits | No | The paper uses standard benchmark datasets like WIKITEXT-103, CIFAR10, CIFAR100, and MNIST, but does not explicitly state the training, validation, and test splits (e.g., percentages or exact sample counts) within the provided text. |
| Hardware Specification | No | No specific hardware details such as GPU models, CPU models, or memory specifications were mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions models and optimizers like 'GPT-2 like architecture' and 'Adam W', but does not provide specific version numbers for software dependencies or libraries (e.g., PyTorch, TensorFlow, Python versions). |
| Experiment Setup | Yes | Table 6: Default experimental settings for the GPT-2 model used to learn the WIKITEXT-103 task. ... Table 8: Default experimental settings for the RESNET18 model used to learn the CIFAR10 task. |