Dataset distillation creates a tiny synthetic training set that, when used with a fixed network initialization, produces models whose performance approximates that of models trained on the full original dataset.
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The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.
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Dataset Distillation
Dataset distillation creates a tiny synthetic training set that, when used with a fixed network initialization, produces models whose performance approximates that of models trained on the full original dataset.
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Adaptive Influence-Based Borrowing Framework for Improving Treatment Effect Estimation in RCTs Using External Controls
The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.