Alexander Immer

49 papers A* 18A 1Misc 1Journal 26Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Forecasting Whole-Brain Neuronal Activity from Volumetric Video. CoRR Alexander Immer, Jan-Matthis Lueckmann, Alex Bo-Yuan Chen, Peter H. Li, Mariela D. Petkova, Nirmala A. Iyer, Aparna Dev, Gudrun Ihrke, Woohyun Park, Alyson Petruncio, Aubrey Weigel, Wyatt Korff, Florian Engert, Jeff W. Lichtman, Misha B. Ahrens, Viren Jain, Michal Januszewski
2025	A*	`conf`	Influence Functions for Scalable Data Attribution in Diffusion Models. ICLR Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner
2025	A*	`conf`	ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish. ICLR 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 B. Ahrens, Michal Januszewski, Viren Jain
2025	J	`jnl`	ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish. CoRR 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 W. Lichtman, Misha B. Ahrens, Michal Januszewski, Viren Jain
2024	—	`—`	Advances in Bayesian Model Selection and Uncertainty Estimation for Deep Learning. Alexander Immer
2024	Misc	`conf`	Hodge-Aware Contrastive Learning. ICASSP Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
2024	A*	`conf`	Improving Neural Additive Models with Bayesian Principles. ICML Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2024	J	`jnl`	Influence Functions for Scalable Data Attribution in Diffusion Models. CoRR Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR 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
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 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
2024	J	`jnl`	Probabilistic pathway-based multimodal factor analysis. Bioinform. Alexander Immer, Stefan G. Stark, Francis Jacob, Ximena Bonilla, Tinu Thomas, André Kahles, Sandra Götze, Emanuela S. Milani, Bernd Wollscheid, Tumor Profiler Consortium, Gunnar Rätsch, Kjong-Van Lehmann
2024	J	`jnl`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	A*	`conf`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. NeurIPS Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	A*	`conf`	Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. ICLR Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
2024	J	`jnl`	Uncertainty-Penalized Direct Preference Optimization. CoRR Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Rätsch
2023	A*	`conf`	Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. NeurIPS Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
2023	J	`jnl`	Hodge-Aware Contrastive Learning. CoRR Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
2023	A*	`conf`	Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
2023	J	`jnl`	Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
2023	J	`jnl`	Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2023	A*	`conf`	Learning Layer-wise Equivariances Automatically using Gradients. NeurIPS Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
2023	J	`jnl`	Learning Layer-wise Equivariances Automatically using Gradients. CoRR Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
2023	A*	`conf`	On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
2023	J	`jnl`	Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
2023	A*	`conf`	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
2023	J	`jnl`	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
2023	J	`jnl`	Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. CoRR Alexandru Meterez, Amir Joudaki, Francesco Orabona, Alexander Immer, Gunnar Rätsch, Hadi Daneshmand
2023	J	`jnl`	Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. CoRR Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
2022	A*	`conf`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
2022	J	`jnl`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
2022	J	`jnl`	On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
2022	—	`conf`	Probing as Quantifying Inductive Bias. ACL (1) Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
2021	A	`conf`	Improving predictions of Bayesian neural nets via local linearization. AISTATS Alexander Immer, Maciej Korzepa, Matthias Bauer
2021	A*	`conf`	Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
2021	J	`jnl`	Laplace Redux - Effortless Bayesian Deep Learning. CoRR Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
2021	J	`jnl`	Pathologies in priors and inference for Bayesian transformers. CoRR Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
2021	J	`jnl`	Probing as Quantifying the Inductive Bias of Pre-trained Representations. CoRR Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
2021	A*	`conf`	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
2021	J	`jnl`	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
2020	A*	`conf`	Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
2020	J	`jnl`	Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
2020	J	`jnl`	Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks. CoRR Alexander Immer
2020	J	`jnl`	Improving predictions of Bayesian neural networks via local linearization. CoRR Alexander Immer, Maciej Korzepa, Matthias Bauer
2020	A*	`conf`	Sub-Matrix Factorization for Real-Time Vote Prediction. KDD Alexander Immer, Victor Kristof, Matthias Grossglauser, Patrick Thiran
2019	A*	`conf`	Approximate Inference Turns Deep Networks into Gaussian Processes. NeurIPS Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
2019	J	`jnl`	Approximate Inference Turns Deep Networks into Gaussian Processes. CoRR Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
2019	A*	`conf`	Efficient learning of smooth probability functions from Bernoulli tests with guarantees. ICML Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher
2017	J	`jnl`	Generative Interest Estimation for Document Recommendations. CoRR Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser
2015	—	`conf`	Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides. UrbanGIS@SIGSPATIAL Keven Richly, Ralf Teusner, Alexander Immer, Fabian Windheuser, Lennard Wolf