Alexander Immer

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Vincent FortuinPrincipal Investigator, Helmholtz AI & TU MunichVerified email at tum.de
Runa EschenhagenPhD student, University of CambridgeVerified email at cam.ac.uk
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Philipp HennigUniversity of TübingenVerified email at uni-tuebingen.de
Matthias BauerDeepMind, LondonVerified email at google.com
Agustinus KristiadiPostdoc, Vector InstituteVerified email at vectorinstitute.ai
Erik DaxbergerAppleVerified email at apple.com
Tycho F. A. van der OuderaaImperial College London, University of OxfordVerified email at imperial.ac.uk
Mark van der WilkAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Siddharth SwaroopPostdoctoral Fellow, Harvard UniversityVerified email at seas.harvard.edu
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Alexander MarxProfessor, TU DortmundVerified email at tu-dortmund.de
Lucas Torroba HennigenMITVerified email at mit.edu
Ryan CotterellETH ZürichVerified email at inf.ethz.ch
Victor KristofResearch Scientist at EPFLVerified email at epfl.ch
Patrick ThiranProfessor, School of Communication and Computer Science, EPFL (Ecole Polytechnique Federale Lausanne)Verified email at epfl.ch
Volkan CevherAssociate Professor, LIONS, EPFL. Amazon Scholar.Verified email at epfl.ch
Kjong-Van LehmannCaner Research Center Cologne Essen - Partner Site University Hospital RWTH AachenVerified email at ukaachen.de

Alexander Immer

PhD student, ETH Zürich, Max Planck Institute for Intelligent Systems

Verified email at inf.ethz.ch - Homepage

Machine Learning Approximate Bayesian Inference


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Laplace Redux--Effortless Bayesian Deep Learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, ... NeurIPS, 2021	248	2021
Continual deep learning by functional regularisation of memorable past P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan NeurIPS, 2020	131	2020
Improving predictions of Bayesian neural nets via local linearization A Immer, M Korzepa, M Bauer AISTATS, 703-711, 2021	129	2021
Approximate inference turns deep networks into gaussian processes ME Khan, A Immer, E Abedi, M Korzepa NeurIPS, 2019	119	2019
Scalable marginal likelihood estimation for model selection in deep learning A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan ICML, 2021	101	2021
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations A Immer, TFA van der Ouderaa, V Fortuin, G Rätsch, M van der Wilk NeurIPS, 2022	35	2022
On the Identifiability and Estimation of Causal Location-Scale Noise Models A Immer, C Schultheiss, JE Vogt, B Schölkopf, P Bühlmann, A Marx ICML 2023, 2022	33	2022
Probing as Quantifying the Inductive Bias of Pre-trained Representations A Immer, LT Hennigen, V Fortuin, R Cotterell ACL, 2022	22*	2022
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels A Immer, TFA van der Ouderaa, M van der Wilk, G Rätsch, B Schölkopf ICML, 2023	12	2023
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures R Eschenhagen, A Immer, RE Turner, F Schneider, P Hennig NeurIPS, 2023	10	2023
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ... Forty-first International Conference on Machine Learning, 2024	9*	2024
Pathologies in priors and inference for Bayesian transformers T Cinquin, A Immer, M Horn, V Fortuin AABI 2022, 2021	9	2021
Optimizing routes of public transportation systems by analyzing the data of taxi rides K Richly, R Teusner, A Immer, F Windheuser, L Wolf Proceedings of the 1st international ACM SIGSPATIAL workshop on smart cities …, 2015	9	2015
Sub-Matrix Factorization for Real-Time Vote Prediction A Immer, V Kristof, M Grossglauser, P Thiran KDD, 2280-2290, 2020	7	2020
Learning Layer-wise Equivariances Automatically using Gradients TFA van der Ouderaa, A Immer, M van der Wilk NeurIPS, 2023	6	2023
Promises and pitfalls of the linearized Laplace in Bayesian optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin arXiv preprint arXiv:2304.08309, 2023	6	2023
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks A Immer, E Palumbo, A Marx, JE Vogt NeurIPS, 2023	5	2023
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion A Meterez, A Joudaki, F Orabona, A Immer, G Rätsch, H Daneshmand ICLR 2024, 2024	3	2024
Improving Neural Additive Models with Bayesian Principles K Bouchiat, A Immer, H Yèche, G Rätsch, V Fortuin ICML, 2024	3*	2024
Efficient learning of smooth probability functions from Bernoulli tests with guarantees P Rolland, A Kavis, A Immer, A Singla, V Cevher ICML, 2019	3	2019

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