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Balancing Expressivity and Overfitting in Quantum Gaussian Process Regression
Active learning is a paradigm of machine learning that can be utilized to model expensive black-box functions by training a surrogate model from actively queried training points. The performance of this framework depends heavily on the choice of the surrogate model. When the surrogate is Gaussian Pr
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