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ORCIDAlexander Immer
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2020 – today
- 2024
[c19]Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi:
Hodge-Aware Contrastive Learning. ICASSP 2024: 9746-9750- [c18]Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand:
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. ICLR 2024 - [c17]Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin:
Improving Neural Additive Models with Bayesian Principles. ICML 2024 - [c16]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: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i21]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 abs/2402.00809 (2024) - [i20]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 abs/2402.15978 (2024) - 2023
- [c15]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: 14316-14332 - [c14]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: 14333-14352 - [c13]Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig:
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS 2023 - [c12]Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt:
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. NeurIPS 2023 - [c11]Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk:
Learning Layer-wise Equivariances Automatically using Gradients. NeurIPS 2023 - [i19]Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin:
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR abs/2304.08309 (2023) - [i18]Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin:
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR abs/2305.16905 (2023) - [i17]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 abs/2306.03968 (2023) - [i16]Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi:
Hodge-Aware Contrastive Learning. CoRR abs/2309.07364 (2023) - [i15]Alexandru Meterez, Amir Joudaki
, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand:
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. CoRR abs/2310.02012 (2023) - [i14]Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk:
Learning Layer-wise Equivariances Automatically using Gradients. CoRR abs/2310.06131 (2023) - [i13]Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig:
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR abs/2311.00636 (2023) - [i12]Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin:
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. CoRR abs/2312.00232 (2023) - 2022
- [c10]Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell:
Probing as Quantifying Inductive Bias. ACL (1) 2022: 1839-1851 - [c9]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 - [i11]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 abs/2202.10638 (2022) - [i10]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 abs/2210.09054 (2022) - 2021
- [c8]Alexander Immer, Maciej Korzepa, Matthias Bauer:
Improving predictions of Bayesian neural nets via local linearization. AISTATS 2021: 703-711 - [c7]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML 2021: 4563-4573 - [c6]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS 2021: 20089-20103 - [i9]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR abs/2104.04975 (2021) - [i8]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. CoRR abs/2106.14806 (2021) - [i7]Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin:
Pathologies in priors and inference for Bayesian transformers. CoRR abs/2110.04020 (2021) - [i6]Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell:
Probing as Quantifying the Inductive Bias of Pre-trained Representations. CoRR abs/2110.08388 (2021) - 2020
- [c5]Alexander Immer, Victor Kristof, Matthias Grossglauser, Patrick Thiran:
Sub-Matrix Factorization for Real-Time Vote Prediction. KDD 2020: 2280-2290 - [c4]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 - [i5]Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan:
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR abs/2004.14070 (2020) - [i4]Alexander Immer:
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks. CoRR abs/2007.11994 (2020) - [i3]Alexander Immer, Maciej Korzepa, Matthias Bauer:
Improving predictions of Bayesian neural networks via local linearization. CoRR abs/2008.08400 (2020)
2010 – 2019
- 2019
- [c3]Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher:
Efficient learning of smooth probability functions from Bernoulli tests with guarantees. ICML 2019: 5459-5467 - [c2]Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa:
Approximate Inference Turns Deep Networks into Gaussian Processes. NeurIPS 2019: 3088-3098 - [i2]Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa:
Approximate Inference Turns Deep Networks into Gaussian Processes. CoRR abs/1906.01930 (2019) - 2017
- [i1]Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser:
Generative Interest Estimation for Document Recommendations. CoRR abs/1711.10327 (2017) - 2015
- [c1]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: 70-76
Coauthor Index
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last updated on 2024-09-04 01:24 CEST by the dblp team
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