Alexander Immer

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 37

Top Topics

Probability Functions

Top Venues

Publications

Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. ICLR (2024)
Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR (2024)
Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
Hodge-Aware Contrastive Learning. ICASSP (2024)
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML (2023)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR (2023)
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR (2023)
Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. CoRR (2023)
Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. NeurIPS (2023)
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR (2023)
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR (2023)
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML (2023)
Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. CoRR (2023)
Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
Hodge-Aware Contrastive Learning. CoRR (2023)
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Learning Layer-wise Equivariances Automatically using Gradients. CoRR (2023)
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Learning Layer-wise Equivariances Automatically using Gradients. NeurIPS (2023)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS (2023)
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR (2022)
Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR (2022)
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
Probing as Quantifying Inductive Bias. ACL (1) (2022)
Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS (2022)
Alexander Immer, Maciej Korzepa, Matthias Bauer
Improving predictions of Bayesian neural nets via local linearization. AISTATS (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML (2021)
Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
Pathologies in priors and inference for Bayesian transformers. CoRR (2021)
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
Probing as Quantifying the Inductive Bias of Pre-trained Representations. CoRR (2021)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS (2021)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. CoRR (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR (2021)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS (2020)
Alexander Immer, Victor Kristof, Matthias Grossglauser, Patrick Thiran
Sub-Matrix Factorization for Real-Time Vote Prediction. KDD (2020)
Alexander Immer
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks. CoRR (2020)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR (2020)
Alexander Immer, Maciej Korzepa, Matthias Bauer
Improving predictions of Bayesian neural networks via local linearization. CoRR (2020)
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
Approximate Inference Turns Deep Networks into Gaussian Processes. NeurIPS (2019)
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
Approximate Inference Turns Deep Networks into Gaussian Processes. CoRR (2019)
Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher
Efficient learning of smooth probability functions from Bernoulli tests with guarantees. ICML (2019)
Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser
Generative Interest Estimation for Document Recommendations. CoRR (2017)
Keven Richly, Ralf Teusner, Alexander Immer, Fabian Windheuser, Lennard Wolf
Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides. UrbanGIS@SIGSPATIAL (2015)