Alexander Immer

According to our database, Alexander Immer authored at least 31 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Uncertainty-Penalized Direct Preference Optimization.

Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Rätsch

CoRR, 2024

Influence Functions for Scalable Data Attribution in Diffusion Models.

Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner

CoRR, 2024

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood.

Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin

CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.

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

CoRR, 2024

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improving Neural Additive Models with Bayesian Principles.

Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion.

Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Hodge-Aware Contrastive Learning.

Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks.

Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin

CoRR, 2023

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference.

Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin

CoRR, 2023

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization.

Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin

CoRR, 2023

Learning Layer-wise Equivariances Automatically using Gradients.

Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Effective Bayesian Heteroscedastic Regression with Deep Neural Networks.

Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures.

Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Identifiability and Estimation of Causal Location-Scale Noise Models.

Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx

Proceedings of the International Conference on Machine Learning, 2023

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels.

Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf

Proceedings of the International Conference on Machine Learning, 2023

2022

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations.

Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Probing as Quantifying Inductive Bias.

Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Probing as Quantifying the Inductive Bias of Pre-trained Representations.

Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell

CoRR, 2021

Pathologies in priors and inference for Bayesian transformers.

Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin

CoRR, 2021

Laplace Redux - Effortless Bayesian Deep Learning.

Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.

Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan

Proceedings of the 38th International Conference on Machine Learning, 2021

Improving predictions of Bayesian neural nets via local linearization.

Alexander Immer, Maciej Korzepa, Matthias Bauer

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Improving predictions of Bayesian neural networks via local linearization.

Alexander Immer, Maciej Korzepa, Matthias Bauer

CoRR, 2020

Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks.

Alexander Immer

CoRR, 2020

Continual Deep Learning by Functional Regularisation of Memorable Past.

Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sub-Matrix Factorization for Real-Time Vote Prediction.

Alexander Immer, Victor Kristof, Matthias Grossglauser, Patrick Thiran

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Approximate Inference Turns Deep Networks into Gaussian Processes.

Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher

Proceedings of the 36th International Conference on Machine Learning, 2019

2017

Generative Interest Estimation for Document Recommendations.

Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser

CoRR, 2017

2015

Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides.

Keven Richly, Ralf Teusner, Alexander Immer, Fabian Windheuser, Lennard Wolf

Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, 2015