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ORCIDSebastian W. Ober
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2020 – today
- 2024
[i14]Sebastian W. Ober:
Towards Improved Variational Inference for Deep Bayesian Models. CoRR abs/2401.12418 (2024)- [i13]Sebastian W. Ober, Artem Artemev, Marcel Wagenländer, Rudolfs Grobins, Mark van der Wilk:
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes. CoRR abs/2402.09849 (2024) - [i12]Alexandra Gessner, Sebastian W. Ober, Owen Vickery, Dino Oglic, Talip Uçar:
Active learning for affinity prediction of antibodies. CoRR abs/2406.07263 (2024) - 2023
- [c9]Henry B. Moss, Sebastian W. Ober, Victor Picheny:
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation. AISTATS 2023: 5213-5230 - [c8]Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison:
An improved variational approximate posterior for the deep Wishart process. UAI 2023: 1555-1563 - [i11]Henry B. Moss, Sebastian W. Ober, Victor Picheny:
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation. CoRR abs/2301.10123 (2023) - [i10]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:
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow. CoRR abs/2302.08436 (2023) - [i9]Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison:
An Improved Variational Approximate Posterior for the Deep Wishart Process. CoRR abs/2305.14454 (2023) - 2022
- [c7]Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. AISTATS 2022: 3542-3555 - [c6]Vincent Fortuin
, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. ICLR 2022 - [i8]Henry B. Moss, Sebastian W. Ober, Victor Picheny:
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation. CoRR abs/2206.02437 (2022) - 2021
- [c5]Laurence Aitchison, Adam X. Yang, Sebastian W. Ober:
Deep Kernel Processes. ICML 2021: 130-140 - [c4]Sebastian W. Ober, Laurence Aitchison:
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. ICML 2021: 8248-8259 - [c3]Sebastian W. Ober, Laurence Aitchison:
A variational approximate posterior for the deep Wishart process. NeurIPS 2021: 6567-6579 - [c2]Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The promises and pitfalls of deep kernel learning. UAI 2021: 1206-1216 - [i7]Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The Promises and Pitfalls of Deep Kernel Learning. CoRR abs/2102.12108 (2021) - [i6]Pola Elisabeth Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark van der Wilk:
Last Layer Marginal Likelihood for Invariance Learning. CoRR abs/2106.07512 (2021) - [i5]Sebastian W. Ober, Laurence Aitchison:
A variational approximate posterior for the deep Wishart process. CoRR abs/2107.10125 (2021) - 2020
- [i4]Sebastian W. Ober, Laurence Aitchison:
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. CoRR abs/2005.08140 (2020) - [i3]Laurence Aitchison, Adam X. Yang, Sebastian W. Ober:
Deep kernel processes. CoRR abs/2010.01590 (2020) - [i2]David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk:
Understanding Variational Inference in Function-Space. CoRR abs/2011.09421 (2020)
2010 – 2019
- 2019
- [i1]Sebastian W. Ober, Carl Edward Rasmussen:
Benchmarking the Neural Linear Model for Regression. CoRR abs/1912.08416 (2019) - 2017
- [c1]Ziwei Zhu, Sebastian W. Ober, Roozbeh Jafari:
Modeling and detecting student attention and interest level using wearable computers. BSN 2017: 13-18
Coauthor Index
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