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hi everyone I'm Jun thanks for coming to my talk today for our work sulex a visual analytics approach to understand local model explanations as a subpopulation level local explanation techniques are popular to interpret a machine learning model's behavior on a single instance for example here sh values indicate how the model output is attribute to different feature values however local explanations do not scale to the understanding of model behaviors or the whole data set to understand the patterns of a large set of local explanations it is natural to conduct subpopulation level analysis for example we can apply clustering and projection techniques however it is possible that we cannot see any significant group structure in the data due to high dimensionality and the spacity of the local explanations to overcome this issue we are motivated to to introduce human input to refine the results of the computed subpopulations for example a domain expert knows that prol line is more important

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