Immer, Alexander

22 publications

ICLR 2025 Influence Functions for Scalable Data Attribution in Diffusion Models Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner

ICLR 2025 ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish Jan-Matthis Lueckmann, Alexander Immer, Alex Bo-Yuan Chen, Peter H. Li, Mariela D Petkova, Nirmala A Iyer, Luuk Willem Hesselink, Aparna Dev, Gudrun Ihrke, Woohyun Park, Alyson Petruncio, Aubrey Weigel, Wyatt Korff, Florian Engert, Jeff Lichtman, Misha Ahrens, Michal Januszewski, Viren Jain

ICML 2024 Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Ratsch, Vincent Fortuin

ICML 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 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

NeurIPS 2024 Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood Rayen Dhahri, Alexander Immer, Betrand Charpentier, Stephan Günnemann, Vincent Fortuin

ICLR 2024 Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Ratsch, Hadi Daneshmand

NeurIPSW 2024 Uncertainty-Penalized Direct Preference Optimization Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Ratsch

NeurIPSW 2024 Uncertainty-Penalized Direct Preference Optimization Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Ratsch

NeurIPS 2023 Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia Vogt

NeurIPS 2023 Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures Runa Eschenhagen, Alexander Immer, Richard Turner, Frank Schneider, Philipp Hennig

NeurIPS 2023 Learning Layer-Wise Equivariances Automatically Using Gradients Tycho van der Ouderaa, Alexander Immer, Mark van der Wilk

ICML 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

ICML 2023 Stochastic Marginal Likelihood Gradients Using Neural Tangent Kernels Alexander Immer, Tycho F. A. Van Der Ouderaa, Mark Van Der Wilk, Gunnar Ratsch, Bernhard Schölkopf

NeurIPS 2022 Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer, Tycho van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk

AISTATS 2021 Improving Predictions of Bayesian Neural Nets via Local Linearization Alexander Immer, Maciej Korzepa, Matthias Bauer

NeurIPS 2021 Laplace Redux - Effortless Bayesian Deep Learning Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

NeurIPSW 2021 Pathologies in Priors and Inference for Bayesian Transformers Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin

ICML 2021 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Khan Mohammad Emtiyaz

NeurIPS 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

ICMLW 2020 Continual Deep Learning by Functional Regularisation of Memorable past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

NeurIPS 2019 Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa

ICML 2019 Efficient Learning of Smooth Probability Functions from Bernoulli Tests with Guarantees Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher