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Overview

This repository accompanies our paper, Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems, presented at ICML 2020's Law and Machine Learning Workshop.

Paper

We include the raw LaTex and associated with our paper, which can also be found on arXiv.

Figures

We also include the simple scratch code we used to generate the graph figures in the paper. In order to run this code, please be sure to have Julialang >=1.4 installed (follow the instructions on their website) and follow these steps:

  • Open a terminal / command line program of your choice. Clone this directory to a convenient location, and then change directories into the figures subdirectory.

  • We need to then make sure that the appropriate julia dependencies are loaded on your machine. To do so, run julia setup.jl. Note: This only needs to be done the first time you run the graphing code.

  • To run the code we used for generating Figure 3, which explains sampling via flipping a biased coin, run julia coin.jl. This will output the resulting figure in flip.pdf. The flip.pdf included in this repository is the one we use in the paper.

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Scratchpad for generating figures for ICML-LML Workshop paper

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