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cs.LG2022
Optimistic Optimization of Gaussian Process Samples
Julia Grosse, Cheng Zhang, Philipp Hennig
Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions. A competing, computationally more eff…
cs.LG2022★ 4 cited
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
Emilia Magnani, Nicholas Krämer, Runa Eschenhagen +2
Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of t…