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
Continual deep learning by functional regularisation of memorable past
P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan
NeurIPS, 2020
Improving predictions of Bayesian neural nets via local linearization
A Immer, M Korzepa, M Bauer
AISTATS, 703-711, 2021
Approximate inference turns deep networks into gaussian processes
ME Khan, A Immer, E Abedi, M Korzepa
NeurIPS, 2019
Scalable marginal likelihood estimation for model selection in deep learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
ICML, 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
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
Probing as Quantifying the Inductive Bias of Pre-trained Representations
A Immer*, LT Hennigen*, V Fortuin, R Cotterell
ACL, 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
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
R Eschenhagen, A Immer, RE Turner, F Schneider, P Hennig
NeurIPS, 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
Pathologies in priors and inference for Bayesian transformers
T Cinquin, A Immer, M Horn, V Fortuin
AABI 2022, 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
Sub-Matrix Factorization for Real-Time Vote Prediction
A Immer*, V Kristof*, M Grossglauser, P Thiran
KDD, 2280-2290, 2020
Learning Layer-wise Equivariances Automatically using Gradients
TFA van der Ouderaa, A Immer, M van der Wilk
NeurIPS, 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
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
A Immer, E Palumbo, A Marx, JE Vogt
NeurIPS, 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
Improving Neural Additive Models with Bayesian Principles
K Bouchiat, A Immer, H Yèche, G Rätsch, V Fortuin
ICML, 2024
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks
A Immer
École polytechnique fédérale de Lausanne (EPFL), 2020
The system can't perform the operation now. Try again later.
Articles 1–20