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Towards Fair and Calibrated Models
Further, we show that ensuring group-wise calibration with respect to the sensitive attributes automatically results in a fair model under our definition.
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
One of the unique properties of tabular LLP is the ability to create feature bags where all the instances in a bag have the same value for a given feature.
Label Differential Privacy via Aggregation
Further, the $\ell_2^2$-regressor which minimizes the loss on the aggregated dataset has a loss within $\left(1 + o(1)\right)$-factor of the optimum on the original dataset w. p.
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