Gaussian Processes for Machine Learning

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Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, methodology): “Laplace approximation and GP inference with DNN Laplace approximation has a long history in GP and NN literature [73, 17, 61, 49, 63], and the theoretical connection between a Laplaceapproximated DNN and GP has being explored recently [40].”
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (2019, neutral): “Bayesian optimization is a leading technique for zeroth order optimization when function queries are expensive [43, 13], and it has seen great success in hyperparameter optimization for deep learning [43, 14, 31].”
Learning explanations that are hard to vary (2020, neutral): “A Gaussian Process (Rasmussen, 2003) for example would require recomputing the entire solution from scratch, as all interactions are considered.”
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (2019, positive): “Except perhaps for Gaussian process regression models [22] that rely on a probabilistic formulation for quantifying predictive uncertainty, most existing approaches use theoretical error bounds to assess the accuracy of the surrogate model predictions and are formulated based on rather limiting assu”
Balanced Policy Evaluation and Learning (2017, positive): “Moreover, if p = 2 and μt has a Gaussian process prior [44] with mean ft and covariance γtKt then”