12 citations · 21 across the 2 of their papers we have counts for
3 papers
The Promises and Pitfalls of Deep Kernel Learning
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
Deep kernel learning (DKL) and related techniques aim to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One cr…
Understanding Variational Inference in Function-Space
David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso +1
Recent work has attempted to directly approximate the `function-space' or predictive posterior distribution of Bayesian models, without approximating the posterior distribution ove…
Benchmarking the Neural Linear Model for Regression
Sebastian W. Ober, Carl Edward Rasmussen
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforceme…