Towards Improved Variational Inference for Deep Bayesian Models von Ober, Sebastian W

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Big Batch Bayesian Active Learning by Considering Predictive Probabilities von Ober, Sebastian W, Power, Samuel, Diethe, Tom, Moss, Henry B

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A variational approximate posterior for the deep Wishart process von Ober, Sebastian W, Aitchison, Laurence

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Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes von Ober, Sebastian W, Aitchison, Laurence

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

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Active learning for affinity prediction of antibodies von Gessner, Alexandra, Ober, Sebastian W, Vickery, Owen, Oglić, Dino, Uçar, Talip

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Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation von Moss, Henry B, Ober, Sebastian W, Picheny, Victor

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Recommendations for Baselines and Benchmarking Approximate Gaussian Processes von Ober, Sebastian W, Artemev, Artem, Wagenländer, Marcel, Grobins, Rudolfs, van der Wilk, Mark

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Benchmarking the Neural Linear Model for Regression von Ober, Sebastian W, Rasmussen, Carl Edward

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A variational approximate posterior for the deep Wishart process von Ober, Sebastian W, Aitchison, Laurence

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The Promises and Pitfalls of Deep Kernel Learning von Ober, Sebastian W, Rasmussen, Carl E, van der Wilk, Mark

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Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes von Ober, Sebastian W, Aitchison, Laurence

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Deep kernel processes von Aitchison, Laurence, Yang, Adam X, Ober, Sebastian W

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Last Layer Marginal Likelihood for Invariance Learning von Schwöbel, Pola, Jørgensen, Martin, Ober, Sebastian W, van der Wilk, Mark

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Understanding Variational Inference in Function-Space von Burt, David R, Ober, Sebastian W, Garriga-Alonso, Adrià, van der Wilk, Mark

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

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Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow von Picheny, Victor, Berkeley, Joel, Moss, Henry B, Stojic, Hrvoje, Granta, Uri, Ober, Sebastian W, Artemev, Artem, Ghani, Khurram, Goodall, Alexander, Paleyes, Andrei, Vakili, Sattar, Pascual-Diaz, Sergio, Markou, Stratis, Qing, Jixiang, Loka, Nasrulloh R. B. S, Couckuyt, Ivo

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Active learning for affinity prediction of antibodies von Gessner, Alexandra, Ober, Sebastian W, Vickery, Owen, Oglić, Dino, Uçar, Talip

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Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation von Moss, Henry B, Ober, Sebastian W, Picheny, Victor

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Recommendations for Baselines and Benchmarking Approximate Gaussian Processes von Ober, Sebastian W, Artemev, Artem, Wagenländer, Marcel, Grobins, Rudolfs, van der Wilk, Mark

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