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We propose probabilistic models that can extrapolate learning curves ofiterative machine learning algorithms, such as stochastic gradient descent fortraining deep networks, based on training data with variable-length learningcurves. We study instantiations of this framework based on random forests andBayesian recurrent neural networks. Our experiments show that these modelsyield better predictions than state-of-the-art models from the hyperparameteroptimization literature when extrapolating the performance of neural networkstrained with different hyperparameter settings.

Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings