Introduces a framework for constructing ML models via interpretable steps, generalizes standard proxies into a parametrized family of measures, and quantifies the accuracy-interpretability tradeoff via practical algorithms.
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cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
An optimization framework decomposes linear models into increasing-complexity sequences using coordinate updates to generate parametrized interpretability metrics.
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The Price of Interpretability
Introduces a framework for constructing ML models via interpretable steps, generalizes standard proxies into a parametrized family of measures, and quantifies the accuracy-interpretability tradeoff via practical algorithms.
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Optimal Explanations of Linear Models
An optimization framework decomposes linear models into increasing-complexity sequences using coordinate updates to generate parametrized interpretability metrics.