21 citations · 69 across the 14 of their papers we have counts for
17 papers
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation
Michael Dodds, Erik Bodin, Sebastian W. Ober +3
Bayesian optimisation (BO) is a powerful framework for sample-efficient optimisation of expensive black-box functions, but its application to high-dimensional discrete scientific d…
Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
Henry B. Moss, Sebastian W. Ober, Tom Diethe
Bayesian optimisation in the latent space of a Variational AutoEncoder (VAE) is a powerful framework for optimisation tasks over complex structured domains, such as the space of sc…
Big Batch Bayesian Active Learning by Considering Predictive Probabilities
Sebastian W. Ober, Samuel Power, Tom Diethe +1
We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to subopt…
Active learning for affinity prediction of antibodies
Alexandra Gessner, Sebastian W. Ober, Owen Vickery +2
The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance…
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
Sebastian W. Ober, Artem Artemev, Marcel Wagenländer +2
Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training…
Towards Improved Variational Inference for Deep Bayesian Models
Sebastian W. Ober
Deep learning has revolutionized the last decade, being at the forefront of extraordinary advances in a wide range of tasks including computer vision, natural language processing,…
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…
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Henry B. Moss, Sebastian W. Ober, Victor Picheny
Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points sever…
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…
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
Henry B. Moss, Sebastian W. Ober, Victor Picheny
Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly pa…
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…
Deep kernel processes
Laurence Aitchison, Adam X. Yang, Sebastian W. Ober
We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distribu…
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian W. Ober, Laurence Aitchison
We consider the optimal approximate posterior over the top-layer weights in a Bayesian neural network for regression, and show that it exhibits strong dependencies on the lower-lay…
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…