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Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators
The main idea is to design different mechanisms for integrating the unlabeled data, and include in each of them a mixing parameter $\alpha$, controlling the weight given to the unlabeled data.
Semi-Supervised Empirical Risk Minimization: Using unlabeled data to improve prediction
The key ideas are carefully considering the null model as a competitor, and utilizing the unlabeled data to determine signal-noise combinations where SSL outperforms both supervised learning and the null model.
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