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Sampling-based inference for large linear models, with application to linearised Laplace


Oct 10, 2022
Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato

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Adapting the Linearised Laplace Model Evidence for Modern Deep Learning


Jun 17, 2022
Javier Antorán, David Janz, James Urquhart Allingham, Erik Daxberger, Riccardo Barbano, Eric Nalisnick, José Miguel Hernández-Lobato

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* Paper appearing at ICML 2022 

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Bandit optimisation of functions in the Matérn kernel RKHS


Mar 02, 2020
David Janz, David R. Burt, Javier González

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* AISTATS 2020, camera ready 

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Learning a Generative Model for Validity in Complex Discrete Structures


Nov 02, 2018
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato

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* Conference paper at ICLR 2018. Code available online 

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Successor Uncertainties: exploration and uncertainty in temporal difference learning


Oct 15, 2018
David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, Sebastian Tschiatschek

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Learning to Drive in a Day


Sep 11, 2018
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah

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* Further results and demo videos can be viewed at: https://wayve.ai/blog/l2diad 

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Actively Learning what makes a Discrete Sequence Valid


Aug 15, 2017
David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato

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* 6 pages, 2 figures 

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Probabilistic structure discovery in time series data


Nov 21, 2016
David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank Wood

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