| Laplace Redux--Effortless Bayesian Deep Learning E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ... NeurIPS 2021, 2021 | 134 | 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 | 99 | 2020 |
| Approximate inference turns deep networks into gaussian processes ME Khan, A Immer, E Abedi, M Korzepa NeurIPS 2019, 2019 | 89 | 2019 |
| Improving predictions of Bayesian neural nets via local linearization A Immer, M Korzepa, M Bauer AISTATS 2021, 703-711, 2021 | 82 | 2021 |
| Scalable marginal likelihood estimation for model selection in deep learning A Immer, M Bauer, V Fortuin, G Rätsch, ME Khan ICML 2021, 2021 | 67 | 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 | 15* | 2022 |
| Probing as Quantifying the Inductive Bias of Pre-trained Representations A Immer*, LT Hennigen*, V Fortuin, R Cotterell ACL 2022, 2022 | 11* | 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 | 10 | 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 | 7 | 2015 |
| Pathologies in priors and inference for Bayesian transformers T Cinquin, A Immer, M Horn, V Fortuin AABI 2022, 2021 | 3 | 2021 |
| Sub-Matrix Factorization for Real-Time Vote Prediction A Immer*, V Kristof*, M Grossglauser, P Thiran KDD 2020, 2280-2290, 2020 | 3 | 2020 |
| Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks A Immer École polytechnique fédérale de Lausanne (EPFL), 2020 | 3 | 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 | 2 | 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 | 1 | 2019 |
| Joint travel mode detection and segmentation using recurrent neural networks A Immer, F Stock, P Wagner | 1 | 2017 |
| Hodge-Aware Contrastive Learning A Möllers, A Immer, V Fortuin, E Isufi arXiv preprint arXiv:2309.07364, 2023 | | 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 | | 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 | | 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 | | 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 | | 2023 |
Vincent FortuinResearch group leader, Helmholtz AIVerified email at helmholtz-muenchen.de
