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ORCIDManuel Haußmann
Person information
- affiliation: University of Southern Denmark, Odense, Denmark
- affiliation (former): Aalto University, Finland
- affiliation (Ph.D.): Heidelberg University, HCI/IWR, Germany
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Books and Theses
- 2021
[b1]Manuel Haußmann:
Bayesian Neural Networks for Probabilistic Machine Learning. University of Heidelberg, Germany, 2021
Conference and Workshop Papers
- 2024
- [c9]Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki:
Estimating treatment effects from single-arm trials via latent-variable modeling. AISTATS 2024: 2926-2934 - 2023
- [c8]Daolang Huang, Manuel Haussmann, Ulpu Remes, St John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi:
Practical Equivariances via Relational Conditional Neural Processes. NeurIPS 2023 - 2022
- [c7]Melih Kandemir, Abdullah Akgül
, Manuel Haußmann, Gozde Unal:
Evidential Turing Processes. ICLR 2022 - 2021
- [c6]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS 2021: 478-486 - 2019
- [c5]Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht:
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR (Poster) 2019 - [c4]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. IJCAI 2019: 2470-2476 - [c3]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI 2019: 563-573 - 2017
- [c2]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR 2017: 810-819 - 2016
- [c1]Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht:
Variational Weakly Supervised Gaussian Processes. BMVC 2016
Informal and Other Publications
- 2024
- [i10]Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki:
Latent variable model for high-dimensional point process with structured missingness. CoRR abs/2402.05758 (2024) - [i9]Abdullah Akgül, Manuel Haußmann, Melih Kandemir:
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. CoRR abs/2406.04088 (2024) - 2023
- [i8]Daolang Huang, Manuel Haussmann, Ulpu Remes, S. T. John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi:
Practical Equivariances via Relational Conditional Neural Processes. CoRR abs/2306.10915 (2023) - [i7]Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki:
Estimating treatment effects from single-arm trials via latent-variable modeling. CoRR abs/2311.03002 (2023) - 2021
- [i6]Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn:
Understanding Event-Generation Networks via Uncertainties. CoRR abs/2104.04543 (2021) - [i5]Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal:
Evidential Turing Processes. CoRR abs/2106.01216 (2021) - 2020
- [i4]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR abs/2006.09914 (2020) - 2019
- [i3]Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir:
Bayesian Prior Networks with PAC Training. CoRR abs/1906.00816 (2019) - [i2]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. CoRR abs/1906.11471 (2019) - 2018
- [i1]Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht:
Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR abs/1805.07654 (2018)
Coauthor Index
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