Matthias Bauer
Matthias Bauer
DeepMind, London
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Understanding probabilistic sparse Gaussian process approximations
M Bauer, M van der Wilk, CE Rasmussen
arXiv preprint arXiv:1606.04820, 2016
Meta-learning probabilistic inference for prediction
J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner
arXiv preprint arXiv:1805.09921, 2018
Resampled priors for variational autoencoders
M Bauer, A Mnih
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Discriminative k-shot learning using probabilistic models
M Bauer, M Rojas-Carulla, JB Swiatkowski, B Schölkopf, RE Turner
2nd Bayesian Deep Learning Workshop, NIPS 2017, 2017
Learning Invariances using the Marginal Likelihood
M van der Wilk, M Bauer, ST John, J Hensman
Advances in Neural Information Processing Systems, 9938-9948, 2018
Ecological feedback in quorum-sensing microbial populations can induce heterogeneous production of autoinducers
M Bauer, J Knebel, M Lechner, P Pickl, E Frey
Elife 6, e25773, 2017
Interpretable and differentially private predictions
F Harder, M Bauer, M Park
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4083-4090, 2020
Improving predictions of Bayesian neural nets via local linearization
A Immer, M Korzepa, M Bauer
International Conference on Artificial Intelligence and Statistics, 703-711, 2021
Automatic estimation of modulation transfer functions
M Bauer, V Volchkov, M Hirsch, B Schcölkopf
2018 IEEE International Conference on Computational Photography (ICCP), 1-12, 2018
Lindblad driving for nonequilibrium steady-state transport for noninteracting quantum impurity models
M Bauer
Bachelor Thesis, University of Munich, 2011
Scalable marginal likelihood estimation for model selection in deep learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
arXiv preprint arXiv:2104.04975, 2021
VERSA: Versatile and efficient few-shot learning
J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner
Advances in Neural Information Processing Systems, 1-9, 2018
Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference
J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner
Meta Learning Workshop, NeurIPS 2018, 2018
Generalized Doubly Reparameterized Gradient Estimators
M Bauer, A Mnih
arXiv preprint arXiv:2101.11046, 2021
Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
M Bauer
University of Cambridge, 2020
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