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Found 3 papers, 1 papers with code
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.
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
We then use Belief Propagation (BP) to marginalize the Gibbs distribution to obtain pseudo labels.
Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
Our third contribution is theoretical, where we show that our weighted entropy term along with prediction loss on the training set approximates test loss under covariate shift.
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