Ober, Sebastian W.

12 publications

ICLRW 2025 Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization Aniruddh Raghu, Sebastian W. Ober, Maxwell Kazman, Hunter Elliott

ICML 2025 Return of the Latent Space COWBOYS: Re-Thinking the Use of VAEs for Bayesian Optimisation of Structured Spaces Henry Moss, Sebastian W. Ober, Tom Diethe

NeurIPSW 2024 Active Learning for Affinity Prediction of Antibodies Alexandra Gessner, Sebastian W. Ober, Owen Niall Vickery, Dino Oglic, Talip Ucar

NeurIPSW 2024 Big Batch Bayesian Active Learning by Considering Predictive Probabilities Sebastian W. Ober, Samuel Power, Tom Diethe, Henry Moss

NeurIPSW 2024 Trieste: Efficiently Exploring the Depths of Black-Box Functions with TensorFlow Henry Moss, Victor Picheny, Hrvoje Stojic, Sebastian W. Ober, Artem Artemev, Andrei Paleyes, Sattar Vakili, Stratis Markou, Jixiang Qing, Nasrulloh Ratu Bagus Satrio Loka, Ivo Couckuyt

UAI 2023 An Improved Variational Approximate Posterior for the Deep Wishart Process Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison

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

AISTATS 2022 Last Layer Marginal Likelihood for Invariance Learning Pola Schwöbel, Martin Jørgensen, Sebastian W. Ober, Mark Van Der Wilk

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

ICML 2021 Deep Kernel Processes Laurence Aitchison, Adam Yang, Sebastian W. Ober

ICML 2021 Global Inducing Point Variational Posteriors for Bayesian Neural Networks and Deep Gaussian Processes Sebastian W Ober, Laurence Aitchison

UAI 2021 The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober, Carl E. Rasmussen, Mark Wilk