Alexander Immer
Alexander Immer
PhD student, ETH Zürich, Max Planck Institute for Intelligent Systems
Verified email at - Homepage
Cited by
Cited by
Laplace Redux--Effortless Bayesian Deep Learning
E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ...
NeurIPS 2021, 2021
Continual deep learning by functional regularisation of memorable past
P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan
NeurIPS 2020, 2020
Approximate inference turns deep networks into gaussian processes
ME Khan, A Immer, E Abedi, M Korzepa
NeurIPS 2019, 2019
Improving predictions of Bayesian neural nets via local linearization
A Immer, M Korzepa, M Bauer
AISTATS 2021, 703-711, 2021
Scalable marginal likelihood estimation for model selection in deep learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
ICML 2021, 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, 2022
Probing as Quantifying the Inductive Bias of Pre-trained Representations
A Immer*, LT Hennigen*, V Fortuin, R Cotterell
ACL 2022, 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
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
Pathologies in priors and inference for Bayesian transformers
T Cinquin, A Immer, M Horn, V Fortuin
AABI 2022, 2021
Sub-Matrix Factorization for Real-Time Vote Prediction
A Immer*, V Kristof*, M Grossglauser, P Thiran
KDD 2020, 2280-2290, 2020
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks
A Immer
École polytechnique fédérale de Lausanne (EPFL), 2020
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
A Immer, TFA van der Ouderaa, M van der Wilk, G Rätsch, B Schölkopf
ICML 2023, 2023
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
P Rolland, A Kavis, A Immer, A Singla, V Cevher
ICML 2019, 2019
Joint travel mode detection and segmentation using recurrent neural networks
A Immer, F Stock, P Wagner
Hodge-Aware Contrastive Learning
A Möllers, A Immer, V Fortuin, E Isufi
arXiv preprint arXiv:2309.07364, 2023
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
A Möllers, A Immer, E Isufi, V Fortuin
Fifth Symposium on Advances in Approximate Bayesian Inference, 2023
Linearized Laplace Inference in Neural Additive Models
K Bouchiat, A Immer, H Yèche, V Fortuin
Fifth Symposium on Advances in Approximate Bayesian Inference, 2023
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference
K Bouchiat, A Immer, H Yèche, G Rätsch, V Fortuin
arXiv preprint arXiv:2305.16905, 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
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