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Sample re-weighting strategies provide a promising mechanism to deal withimperfect training data in machine learning, such as noisily labeled orclass-imbalanced data. One such strategy involves formulating a bi-leveloptimization problem called the meta re-weighting problem, whose goal is tooptimize performance on a small set of perfect pivotal samples, called metasamples. Many approaches have been proposed to efficiently solve this problem.However, all of them assume that a perfect meta sample set is already providedwhile we observe that the selections of meta sample set is performancecritical. In this paper, we study how to learn to identify such a meta sampleset from a large, imperfect training set, that is subsequently cleaned and usedto optimize performance in the meta re-weighting setting. We propose a learningframework which reduces the meta samples selection problem to a weightedK-means clustering problem through rigorously theoretical analysis. We proposetwo clustering methods within our learning framework, Representation-basedclustering method (RBC) and Gradient-based clustering method (GBC), forbalancing performance and computational efficiency. Empirical studiesdemonstrate the performance advantage of our methods over various baselinemethods.

Learning to Select Pivotal Samples for Meta Re-weighting