Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability

6 Feb 2020Prathyush SambaturuAparna GuptaIan DavidsonS. S. RaviAnil VullikantiAndrew Warren

Improving the explainability of the results from machine learning methods has become an important research goal. Here, we study the problem of making clusters more interpretable by extending a recent approach of [Davidson et al., NeurIPS 2018] for constructing succinct representations for clusters... (read more)

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