Manuel Haussmann
I'm a postdoctoral researcher at the University of Southern Denmark in Odense, where I work in the group of Melih Kandemir.
I did my PhD at Heidelberg University with Fred Hamprecht and Melih, and spent two great years at Aalto University in Finland working with Samuel Kaski and Harri Lähdesmäki.
My research focus is on Bayesian probabilistic machine learning with applications ranging from physics over health data to reinforcement learning.
I am a reviewer for most major machine learning conferences (AAAI, AISTATS, ICLR, ICML, NeurIPS), an action editor for TMLR, and a member of the ICBINB initiative.
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dplp /
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github
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Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
Abdullah Akgül, Manuel Haussmann, Melih Kandemir
Conference on Neural Information Processing Systems
, 2024
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Latent mixed-effect models for high-dimensional longitudinal data
Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki
Preprint on ArXiv; extended version of Ong et al. (2024), see below
, 2024
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Learning high-dimensional mixed models via amortized variational inference
Priscilla Ong, Manuel Haussmann, Harri Lähdesmäki
ICML Workshop on Structured Probabilistic Inference & Generative Modeling
, 2024
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PAC-Bayesian Soft Actor-Critic Learning
Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann, Kenny Kazimirzak Brink, Melih Kandemir
Advances in Approximate Bayesian Inference (AABI)
, 2024
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Latent variable model for high-dimensional point process with structured missingness
Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
International Conference on Machine Learning (ICML)
, 2024
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Estimating treatment effects from single-arm trials via latent-variable modeling
Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki
International Conference on Artificial Inteligence and Statistics (AISTATS)
, 2024
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A comparative study of clinical trial and real-world data in patients with diabetic kidney disease
Samu Kurki*, Viivi Halla-Aho*, Manuel Haussmann, Harri Lähdesmäki, Jussi V Leinonen, Miika Koskinen
Nature Scientific Reports
, 2024
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Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang, Manuel Haussmann, Ulpu Remes, ST John, Grégoire Clarté, Kevin Luck, Samuel Kaski, Luigi Acerbi
Neural Information Processing Systems (NeuRIPS)
, 2023
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Understanding Event-Generation Networks via Uncertainties
Marco Bellagente, Manuel Haussmann, Michel Luchmann, Tilman Plehn
SciPost Physics
, 2022
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Evidential Turing Processes
Melih Kandemir, Abdullah Akgül, Manuel Haussmann, Gozde Unal
International Conference on Learning Representations (ICLR)
, 2022
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Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haussmann*, Sebastian Gerwinn*, Andreas Look, Barbara Rakitsch, Melih Kandemir
International Conference on Artificial Intelligence and Statistics (AISTATS)
, 2021
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Bayesian Evidential Deep Learning with PAC Regularization
Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
Advances in Approximate Bayesian Inference (AABI)
, 2021
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Deep-learning jets with uncertainties and more
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson
SciPost Physics
, 2020
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Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
Uncertainty in Artificial Intelligence Conference, 2020
A sampling-free approach to BNN learning via ReLU decomposition.
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Deep Active Learning with Adaptive Acquisition
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
International Joint Conference on Artificial Intelligence (IJCAI), 2019
We introduce a way to adaptively learn the acquisition function for active learning with reinforcement feedback.
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LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Elke Kirschbaum, Manuel Haussmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A Hamprecht
International Conference on Learning Representations (ICLR), 2019
We present LeMoNADe, an end-to-end learned motif detection method directly operating on calcium imaging videos.
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Variational Bayesian Multiple Instance Learning with Gaussian Processes
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Instance level inference with closed-form updates in a Gaussian processed-based model for weakly supervised multiple instance learning and object detection.
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Variational Weakly Supervised Gaussian Processes
Melih Kandemir, Manuel Haussmann, Ferran Diego, Kumar Rajamani, Jeroen Van Der Laak, Fred Hamprecht
British Machine Vision Conference (BMVC), 2016
We introduce a model for multiple instance learning with Gaussian processes for bag-level predictions with closed-form updates.
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