For multiple instance regression and learning from label proportions, optimal data bagging for linear regression reduces approximately to k-means clustering over labels or features, and the mechanisms can be made label-differentially private with a quantified utility cost.
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Aggregating Data for Optimal and Private Learning
For multiple instance regression and learning from label proportions, optimal data bagging for linear regression reduces approximately to k-means clustering over labels or features, and the mechanisms can be made label-differentially private with a quantified utility cost.