Leveraging Importance Weights in Subset Selection

• URL: http://arxiv.org/abs/2301.12052v1
• Date: Sat, 28 Jan 2023 02:07:31 GMT
• Title: Leveraging Importance Weights in Subset Selection
• Authors: Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang
• Abstract summary: We present a subset selection algorithm designed to work with arbitrary model families in a practical batch setting. Our algorithm, IWeS, selects examples by importance sampling where the sampling probability assigned to each example is based on the entropy of models trained on previously selected batches.
• Score: 45.54597544672441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a subset selection algorithm designed to work with arbitrary model families in a practical batch setting. In such a setting, an algorithm can sample examples one at a time but, in order to limit overhead costs, is only able to update its state (i.e. further train model weights) once a large enough batch of examples is selected. Our algorithm, IWeS, selects examples by importance sampling where the sampling probability assigned to each example is based on the entropy of models trained on previously selected batches. IWeS admits significant performance improvement compared to other subset selection algorithms for seven publicly available datasets. Additionally, it is competitive in an active learning setting, where the label information is not available at selection time. We also provide an initial theoretical analysis to support our importance weighting approach, proving generalization and sampling rate bounds.

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