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Alexander Immer
Alexander Immer
PhD student, ETH Zürich, Max Planck Institute for Intelligent Systems
E-mail confirmado em inf.ethz.ch - Página inicial
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Laplace Redux--Effortless Bayesian Deep Learning
E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ...
NeurIPS, 2021
3602021
Improving predictions of Bayesian neural nets via local linearization
A Immer, M Korzepa, M Bauer
AISTATS, 2021
1552021
Continual deep learning by functional regularisation of memorable past
P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan
NeurIPS, 2020
1542020
Approximate inference turns deep networks into gaussian processes
ME Khan, A Immer, E Abedi, M Korzepa
NeurIPS, 2019
1422019
Scalable marginal likelihood estimation for model selection in deep learning
A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan
ICML, 2021
1252021
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, ...
ICML, 2024
51*2024
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
462023
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
452022
Probing as Quantifying the Inductive Bias of Pre-trained Representations
A Immer*, LT Hennigen*, V Fortuin, R Cotterell
ACL, 2022
28*2022
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
R Eschenhagen, A Immer, RE Turner, F Schneider, P Hennig
NeurIPS, 2023
242023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
A Immer, TFA van der Ouderaa, M van der Wilk, G Rätsch, B Schölkopf
ICML, 2023
152023
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
A Immer, E Palumbo, A Marx, JE Vogt
NeurIPS, 2023
142023
Learning Layer-wise Equivariances Automatically using Gradients
TFA van der Ouderaa, A Immer, M van der Wilk
NeurIPS, 2023
132023
Promises and pitfalls of the linearized Laplace in Bayesian optimization
A Kristiadi, A Immer, R Eschenhagen, V Fortuin
arXiv preprint arXiv:2304.08309, 2023
122023
Pathologies in priors and inference for Bayesian transformers
T Cinquin, A Immer, M Horn, V Fortuin
AABI 2022, 2021
112021
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
112015
Improving Neural Additive Models with Bayesian Principles
K Bouchiat, A Immer, H Yèche, G Rätsch, V Fortuin
ICML, 2024
8*2024
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
A Meterez, A Joudaki, F Orabona, A Immer, G Rätsch, H Daneshmand
ICLR, 2024
72024
Sub-Matrix Factorization for Real-Time Vote Prediction
A Immer*, V Kristof*, M Grossglauser, P Thiran
KDD, 2020
72020
Shaving weights with Occam's razor: Bayesian sparsification for neural networks using the marginal likelihood
R Dhahri, A Immer, B Charpentier, S Günnemann, V Fortuin
Advances in Neural Information Processing Systems 37, 24959-24989, 2024
52024
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