Sebastian W. Ober | DeepAI

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Featured Co-authors

Mark van der Wilk
44 publications
Laurence Aitchison
31 publications
Sattar Vakili
24 publications
Victor Picheny
22 publications
Carl Edward Rasmussen
21 publications
David R. Burt
13 publications
Andrei Paleyes
13 publications
Henry B. Moss
13 publications
Artem Artemev
12 publications
Martin Jørgensen
11 publications
Carl E. Rasmussen
8 publications

∙ 02/16/2023

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

We present Trieste, an open-source Python package for Bayesian optimizat...

0 Victor Picheny, et al. ∙

∙ 01/24/2023

Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation

Sparse Gaussian Processes are a key component of high-throughput Bayesia...

0 Henry B. Moss, et al. ∙

∙ 06/06/2022

Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation

Sparse Gaussian Processes are a key component of high-throughput Bayesia...

0 Henry B. Moss, et al. ∙

∙ 07/21/2021

A variational approximate posterior for the deep Wishart process

Recent work introduced deep kernel processes as an entirely kernel-based...

0 Sebastian W. Ober, et al. ∙

∙ 06/14/2021

Last Layer Marginal Likelihood for Invariance Learning

Data augmentation is often used to incorporate inductive biases into mod...

0 Pola Elisabeth Schwöbel, et al. ∙

∙ 02/24/2021

The Promises and Pitfalls of Deep Kernel Learning

Deep kernel learning and related techniques promise to combine the repre...

0 Sebastian W. Ober, et al. ∙

∙ 11/18/2020

Understanding Variational Inference in Function-Space

Recent work has attempted to directly approximate the `function-space' o...

0 David R. Burt, et al. ∙

∙ 10/04/2020

Deep kernel processes

We define deep kernel processes in which positive definite Gram matrices...

0 Laurence Aitchison, et al. ∙

∙ 05/17/2020

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

Variational inference is a popular approach to reason about uncertainty ...

0 Sebastian W. Ober, et al. ∙

∙ 12/18/2019

Benchmarking the Neural Linear Model for Regression

The neural linear model is a simple adaptive Bayesian linear regression ...

0 Sebastian W. Ober, et al. ∙

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