New approach selects sparse features without validation.
problem Overfitting and high storage/compute costs in AI models.
method Phase transition in probability of feature retrieval.
result Flexible approach for various models and loss functions.
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3 results for “validation-free”
SureMap estimates model performance across subpopulations efficiently.
problem Estimating model performance across subpopulations with scarce data.
method Simultaneous Gaussian mean estimation with external data.
result High accuracy in both multi-task and single-task disaggregated evaluations.
Paper introduces a novel approach to generalize models without validation data.
problem Achieving generalization in machine learning without validation data.
method Introduces a new risk measure and a novel criterion for training models.
result Demonstrates that the new approach outperforms popular methods in generalization and estimation tasks.