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b'We talk often about which features in a dataset are most important, but recently a new paper has started making the rounds that turns the idea of importance on its head: Data Shapley is an algorithm for thinking about which examples in a dataset are most important. It makes a lot of intuitive sense: data that\\u2019s just repeating examples that you\\u2019ve already seen, or that\\u2019s noisy or an extreme outlier, might not be that valuable for using to train a machine learning model. But some data is very valuable, it\\u2019s disproportionately useful for the algorithm figuring out what the most important trends are, and Data Shapley is explicitly designed to help machine learning researchers spend their time understanding which data points are most valuable and why.\\n\\nRelevant links:\\nhttp://proceedings.mlr.press/v97/ghorbani19c/ghorbani19c.pdf\\nhttps://blog.acolyer.org/2019/07/15/data-shapley/'