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Sebastian W. Ober
Sebastian W. Ober
Verified email at cam.ac.uk
Title
Cited by
Cited by
Year
Bayesian neural network priors revisited
V Fortuin, A Garriga-Alonso, SW Ober, F Wenzel, G Rätsch, RE Turner, ...
arXiv preprint arXiv:2102.06571, 2021
662021
The promises and pitfalls of deep kernel learning
SW Ober, CE Rasmussen, M van der Wilk
Uncertainty in Artificial Intelligence, 1206-1216, 2021
412021
Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes
SW Ober, L Aitchison
International Conference on Machine Learning, 8248-8259, 2021
312021
Benchmarking the Neural Linear Model for Regression
SW Ober, CE Rasmussen
arXiv preprint arXiv:1912.08416, 2019
302019
Understanding Variational Inference in Function-Space
DR Burt, SW Ober, A Garriga-Alonso, M van der Wilk
arXiv preprint arXiv:2011.09421, 2020
272020
Modeling and detecting student attention and interest level using wearable computers
Z Zhu, S Ober, R Jafari
2017 IEEE 14th international conference on wearable and implantable body …, 2017
242017
Deep kernel processes
L Aitchison, A Yang, SW Ober
International Conference on Machine Learning, 130-140, 2021
182021
Last Layer Marginal Likelihood for Invariance Learning
P Schwöbel, M Jørgensen, SW Ober, M Van Der Wilk
International Conference on Artificial Intelligence and Statistics, 3542-3555, 2022
32022
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
HB Moss, SW Ober, V Picheny
arXiv preprint arXiv:2206.02437, 2022
22022
A variational approximate posterior for the deep Wishart process
SW Ober, L Aitchison
Thirty-Fifth Conference on Neural Information Processing Systems, 2021
2021
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
SW Ober, DR Burt, A Artemev, M van der Wilk
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Articles 1–11