Jeremias Knoblauch
Jeremias Knoblauch
Doctoral Researcher, Oxford-Warwick Stats Programme, University of Warwick & Alan Turing Institute
Verified email at warwick.ac.uk - Homepage
Title
Cited by
Cited by
Year
Generalized variational inference: Three arguments for deriving new posteriors
J Knoblauch, J Jewson, T Damoulas
arXiv preprint arXiv:1904.02063, 2019
602019
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences
J Knoblauch, J Jewson, T Damoulas
arXiv preprint arXiv:1806.02261, 2018
262018
Optimal continual learning has perfect memory and is np-hard
J Knoblauch, H Husain, T Diethe
International Conference on Machine Learning, 5327-5337, 2020
192020
Spatio-temporal Bayesian on-line changepoint detection with model selection
J Knoblauch, T Damoulas
International Conference on Machine Learning, 2718-2727, 2018
182018
Robust Deep Gaussian Processes
J Knoblauch
arXiv preprint arXiv:1904.02303, 2019
92019
Generalized Posteriors in Approximate Bayesian Computation
SM Schmon, PW Cannon, J Knoblauch
arXiv preprint arXiv:2011.08644, 2020
32020
Frequentist consistency of generalized variational inference
J Knoblauch
arXiv preprint arXiv:1912.04946, 2019
22019
Robust generalised Bayesian inference for intractable likelihoods
T Matsubara, J Knoblauch, FX Briol, C Oates
arXiv preprint arXiv:2104.07359, 2021
12021
Transforming Gaussian processes with normalizing flows
J Maro˝as, O Hamelijnck, J Knoblauch, T Damoulas
International Conference on Artificial Intelligence and Statistics, 1081-1089, 2021
12021
Robust Bayesian Inference for Discrete Outcomes with the Total Variation Distance
J Knoblauch, L Vomfell
arXiv preprint arXiv:2010.13456, 2020
12020
Estimating European Temperature Trends
J Knoblauch
Student Undergraduate Research E-journal! 1, 2015
2015
Spatio-temporal inference for high-dimensional non-stationary processes
J Knoblauch
Robust Generalised Bayesian Inference for Intractable Likelihoods
CJ Oates, T Matsubara, J Knoblauch, FX Briol
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Articles 1–13