21 citations · 50 across the 10 of their papers we have counts for
9 papers · 1 filter
An Improved Variational Approximate Posterior for the Deep Wishart Process
Sebastian Ober, Ben Anson, Edward Milsom +1
Deep kernel processes are a recently introduced class of deep Bayesian models that have the flexibility of neural networks, but work entirely with Gram matrices. They operate by al…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
A variational approximate posterior for the deep Wishart process
Sebastian W. Ober, Laurence Aitchison
Recent work introduced deep kernel processes as an entirely kernel-based alternative to NNs (Aitchison et al. 2020). Deep kernel processes flexibly learn good top-layer representat…
Last Layer Marginal Likelihood for Invariance Learning
Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober +1
Data augmentation is often used to incorporate inductive biases into models. Traditionally, these are hand-crafted and tuned with cross validation. The Bayesian paradigm for model…
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…