Manuel Haußmann

According to our database, Manuel Haußmann authored at least 17 papers between 2016 and 2024.

Collaborative distances:

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

2024

Latent mixed-effect models for high-dimensional longitudinal data.

Priscilla Ong, Manuel Haußmann, Otto Lönnroth, Harri Lähdesmäki

CoRR, 2024

Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning.

Abdullah Akgül, Manuel Haußmann, Melih Kandemir

CoRR, 2024

Latent variable model for high-dimensional point process with structured missingness.

Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki

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

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Practical Equivariances via Relational Conditional Neural Processes.

Daolang Huang, Manuel Haussmann, Ulpu Remes, S. T. John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi

CoRR, 2023

Practical Equivariances via Relational Conditional Neural Processes.

Daolang Huang, Manuel Haussmann, Ulpu Remes, St John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi

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

2022

Evidential Turing Processes.

Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Bayesian Neural Networks for Probabilistic Machine Learning.

Manuel Haußmann

PhD thesis, 2021

Understanding Event-Generation Networks via Uncertainties.

Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn

CoRR, 2021

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes.

Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir

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

2019

Bayesian Prior Networks with PAC Training.

Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir

CoRR, 2019

Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation.

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Deep Active Learning with Adaptive Acquisition.

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

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

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Sampling-Free Variational Inference of Bayesian Neural Nets.

Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht

CoRR, 2018

2017

Variational Bayesian Multiple Instance Learning with Gaussian Processes.

Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Variational Weakly Supervised Gaussian Processes.

Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht

Proceedings of the British Machine Vision Conference 2016, 2016