Meta-learning Feature Representations for Adaptive Gaussian Processes via Implicit Differentiation
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We propose Adaptive Deep Kernel Fitting (ADKF), a general framework for learning deep kernels by interpolating between meta-learning and conventional learning. Our approach employs a bilevel optimization objective where we meta-learn feature representations that are generally useful across tasks, in the sense that task-specific Gaussian process models estimated on top of such features achieve the lowest possible predictive loss on average across tasks. We solve the resulting nested optimization problem using the implicit function theorem. We show that ADKF contains Deep Kernel Learning and Deep Kernel Transfer as special cases. Although ADKF is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular optimization tasks.
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