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Challenges and Pitfalls of Bayesian Unlearning


Jul 07, 2022
Ambrish Rawat, James Requeima, Wessel Bruinsma, Richard Turner

* 5 pages, 3 figures, Updatable ML (UpML) Workshop, International Conference on Machine Learning (ICML) 2022 

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Efficient Gaussian Neural Processes for Regression


Aug 24, 2021
Stratis Markou, James Requeima, Wessel Bruinsma, Richard Turner

* 6 pages 

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Bayesian Neural Network Priors Revisited


Feb 12, 2021
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, Laurence Aitchison


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VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data


Jun 21, 2020
Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard Turner, Cheng Zhang


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Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model


Aug 14, 2019
Wenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang


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Invariant Models for Causal Transfer Learning


Sep 24, 2018
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters

* Journal of Machine Learning Research. 19 (2018) 

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Overpruning in Variational Bayesian Neural Networks


Jan 18, 2018
Brian Trippe, Richard Turner

* Presented the Advances in Approximate Bayesian Inference workshop at NIPS 2017 

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Magnetic Hamiltonian Monte Carlo


Aug 19, 2017
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

* 34th International Conference on Machine Learning (ICML 2017) 

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Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs


Mar 20, 2015
Yarin Gal, Richard Turner

* 13 pages, 3 figures 

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