Modeling and detecting student attention and interest level using wearable computers. - Ziwei Zhu 001; Sebastian W. Ober; Roozbeh Jafari (BSN, 2017)

Benchmarking the Neural Linear Model for Regression. - Sebastian W. Ober; Carl Edward Rasmussen (CoRR, 2019)

Understanding Variational Inference in Function-Space. - David R. Burt; Sebastian W. Ober; Adrià Garriga-Alonso; Mark van der Wilk (CoRR, 2020)

Deep Kernel Processes. - Laurence Aitchison; Adam X. Yang; Sebastian W. Ober (ICML, 2021)

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. - Sebastian W. Ober; Laurence Aitchison (ICML, 2021)

A variational approximate posterior for the deep Wishart process. - Sebastian W. Ober; Laurence Aitchison (NeurIPS, 2021)

The promises and pitfalls of deep kernel learning. - Sebastian W. Ober; Carl E. Rasmussen; Mark van der Wilk (UAI, 2021)

Last Layer Marginal Likelihood for Invariance Learning. - Pola Schwöbel; Martin Jørgensen; Sebastian W. Ober; Mark van der Wilk (AISTATS, 2022)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Sebastian W. Ober; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (ICLR, 2022)

Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation. - Henry B. Moss; Sebastian W. Ober; Victor Picheny (CoRR, 2022)

Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation. - Henry B. Moss; Sebastian W. Ober; Victor Picheny (AISTATS, 2023)

An improved variational approximate posterior for the deep Wishart process. - Sebastian W. Ober; Ben Anson; Edward Milsom; Laurence Aitchison (UAI, 2023)

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. - Victor Picheny; Joel Berkeley; Henry B. Moss; Hrvoje Stojic; Uri Granta; Sebastian W. Ober; Artem Artemev; Khurram Ghani; Alexander Goodall; Andrei Paleyes; Sattar Vakili; Sergio Pascual-Diaz; et al. (CoRR, 2023)

Towards Improved Variational Inference for Deep Bayesian Models. - Sebastian W. Ober (CoRR, 2024)

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. - Sebastian W. Ober; Artem Artemev; Marcel Wagenländer; Rudolfs Grobins; Mark van der Wilk (CoRR, 2024)

Active learning for affinity prediction of antibodies. - Alexandra Gessner; Sebastian W. Ober; Owen Vickery; Dino Oglic; Talip Uçar (CoRR, 2024)

Big Batch Bayesian Active Learning by Considering Predictive Probabilities. - Sebastian W. Ober; Samuel Power; Tom Diethe; Henry B. Moss (CoRR, 2025)

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 (CoRR, 2025)