Sebastian W. Ober

According to our database, Sebastian W. Ober authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Active learning for affinity prediction of antibodies.

Alexandra Gessner, Sebastian W. Ober, Owen Vickery, Dino Oglic, Talip Uçar

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

Towards Improved Variational Inference for Deep Bayesian Models.

Sebastian W. Ober

CoRR, 2024

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, Stratis Markou, Jixiang Qing, Nasrulloh R. B. S. Loka, Ivo Couckuyt

CoRR, 2023

An improved variational approximate posterior for the deep Wishart process.

Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison

Proceedings of the Uncertainty in Artificial Intelligence, 2023

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

Henry B. Moss, Sebastian W. Ober, Victor Picheny

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

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

Henry B. Moss, Sebastian W. Ober, Victor Picheny

CoRR, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Last Layer Marginal Likelihood for Invariance Learning.

Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

The promises and pitfalls of deep kernel learning.

Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

A variational approximate posterior for the deep Wishart process.

Sebastian W. Ober, Laurence Aitchison

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Sebastian W. Ober, Laurence Aitchison

Proceedings of the 38th International Conference on Machine Learning, 2021

Deep Kernel Processes.

Laurence Aitchison, Adam X. Yang, Sebastian W. Ober

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Understanding Variational Inference in Function-Space.

David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk

CoRR, 2020

2019

Benchmarking the Neural Linear Model for Regression.

Sebastian W. Ober, Carl Edward Rasmussen

CoRR, 2019

2017

Modeling and detecting student attention and interest level using wearable computers.

Ziwei Zhu, Sebastian W. Ober, Roozbeh Jafari

Proceedings of the 14th IEEE International Conference on Wearable and Implantable Body Sensor Networks, 2017