Manuel Haußmann

18 papers A* 3A 3Journal 10Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Distributional Active Inference. CoRR Abdullah Akgül, Gulcin Baykal, Manuel Haußmann, Mustafa Mert Çelikok, Melih Kandemir
2025	J	`jnl`	Improving Plasticity in Non-stationary Reinforcement Learning with Evidential Proximal Policy Optimization. CoRR Abdullah Akgül, Gulcin Baykal, Manuel Haußmann, Melih Kandemir
2024	J	`jnl`	Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. CoRR Abdullah Akgül, Manuel Haußmann, Melih Kandemir
2024	J	`jnl`	Latent mixed-effect models for high-dimensional longitudinal data. CoRR Priscilla Ong, Manuel Haußmann, Otto Lönnroth, Harri Lähdesmäki
2022	A*	`conf`	Evidential Turing Processes. ICLR Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal
2021	—	`—`	Bayesian Neural Networks for Probabilistic Machine Learning. Manuel Haußmann
2021	J	`jnl`	Evidential Turing Processes. CoRR Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal
2021	A	`conf`	Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
2021	J	`jnl`	Understanding Event-Generation Networks via Uncertainties. CoRR Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn
2020	J	`jnl`	Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
2019	J	`jnl`	Bayesian Prior Networks with PAC Training. CoRR Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir
2019	A*	`conf`	Deep Active Learning with Adaptive Acquisition. IJCAI Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2019	J	`jnl`	Deep Active Learning with Adaptive Acquisition. CoRR Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2019	—	`conf`	LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR (Poster) Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht
2019	A	`conf`	Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2018	J	`jnl`	Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht
2017	A*	`conf`	Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2016	A	`conf`	Variational Weakly Supervised Gaussian Processes. BMVC Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht