214 citations · 1.8k across the 88 of their papers we have counts for
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Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Marvin Pförtner, Ingo Steinwart, Philipp Hennig +1
Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, an…
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…
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…
Wasserstein t-SNE
Fynn Bachmann, Philipp Hennig, Dmitry Kobak
Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographi…
Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner +2
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. Th…
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Monte Carlo (MC) integration is the de facto method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based…