Correlation Aware Sparsified Mean Estimation Using Random Projection
Authors: Shuli Jiang, PRANAY SHARMA, Gauri Joshi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world distributed optimization tasks showcase the superior performance of Rand-Proj Spatial compared to Rand-k-Spatial and other more sophisticated sparsification techniques. We conduct experiments on common distributed optimization tasks, and demonstrate the superior performance of Rand-Proj-Spatial compared to existing sparsification techniques. |
| Researcher Affiliation | Academia | Shuli Jiang Robotics Institute Carnegie Mellon University shulij@andrew.cmu.edu Pranay Sharma ECE Carnegie Mellon University pranaysh@andrew.cmu.edu Gauri Joshi ECE Carnegie Mellon University gaurij@andrew.cmu.edu |
| Pseudocode | No | The paper describes its methods using mathematical equations and textual descriptions, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | All code used for the experiments can be found at https://github.com/11hifish/Rand-Proj-Spatial. |
| Open Datasets | Yes | For both distributed power iteration and distributed k-means, we use the test set of the Fashion-MNIST dataset [56] consisting of 10000 samples. ... We use the UJIndoor dataset 2 for distributed linear regression. |
| Dataset Splits | No | The paper mentions using 'the test set of the Fashion-MNIST dataset' and that datasets are 'split IID across the clients via random shuffling' or 'non-IID'. However, it does not explicitly provide percentages or counts for training, validation, and test splits for the full datasets to reproduce the data partitioning. |
| Hardware Specification | No | The paper states: 'All experiments are conducted in a cluster of 20 machines, each of which has 40 cores.' However, it does not provide specific details such as CPU model, GPU model, or memory specifications. |
| Software Dependencies | No | The paper states: 'The implementation is in Python, mainly based on numpy and scipy.' However, it does not specify version numbers for Python or the libraries (numpy, scipy) used. |
| Experiment Setup | Yes | For Rand-Proj-Spatial, we use the first 50 iterations to estimate β (see Eq. 5)... We repeat the experiments across 10 independent runs... For both distributed power iterations and distributed k-means, we run the experiments for 30 iterations and consider two different settings: n = 10, k = 102 and n = 50, k = 20. For distributed linear regression, we run the experiments for 50 iterations with learning rate 0.001. |