Variational Weakly Supervised Gaussian Processes. - Melih Kandemir; Manuel Haußmann; Ferran Diego; Kumar T. Rajamani; Jeroen van der Laak; Fred A. Hamprecht (BMVC, 2016)

Variational Bayesian Multiple Instance Learning with Gaussian Processes. - Manuel Haußmann; Fred A. Hamprecht; Melih Kandemir (CVPR, 2017)

Sampling-Free Variational Inference of Bayesian Neural Nets. - Melih Kandemir; Manuel Haußmann; Fred A. Hamprecht (CoRR, 2018)

LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. - Elke Kirschbaum; Manuel Haußmann; Steffen Wolf 001; Hannah Sonntag; Justus Schneider; Shehabeldin Elzoheiry; Oliver Kann; Daniel Durstewitz; Fred A. Hamprecht (ICLR (Poster), 2019)

Deep Active Learning with Adaptive Acquisition. - Manuel Haußmann; Fred A. Hamprecht; Melih Kandemir (IJCAI, 2019)

Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. - Manuel Haußmann; Fred A. Hamprecht; Melih Kandemir (UAI, 2019)

Bayesian Prior Networks with PAC Training. - Manuel Haußmann; Sebastian Gerwinn; Melih Kandemir (CoRR, 2019)

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. - Manuel Haußmann; Sebastian Gerwinn; Andreas Look; Barbara Rakitsch; Melih Kandemir (AISTATS, 2021)

Understanding Event-Generation Networks via Uncertainties. - Marco Bellagente; Manuel Haußmann; Michel Luchmann; Tilman Plehn (CoRR, 2021)

Bayesian Neural Networks for Probabilistic Machine Learning. - Manuel Haußmann (2021)

Evidential Turing Processes. - Melih Kandemir; Abdullah Akgül; Manuel Haußmann; Gozde Unal (ICLR, 2022)

Practical Equivariances via Relational Conditional Neural Processes. - Daolang Huang; Manuel Haussmann; Ulpu Remes; St John; Grégoire Clarté; Kevin Sebastian Luck; Samuel Kaski; Luigi Acerbi (NeurIPS, 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)

PAC-Bayesian Soft Actor-Critic Learning. - Bahareh Tasdighi; Abdullah Akgül; Manuel Haussmann; Kenny Kazimirzak Brink; Melih Kandemir (AABI, 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 (AISTATS, 2024)

Latent variable model for high-dimensional point process with structured missingness. - Maksim Sinelnikov; Manuel Haussmann; Harri Lähdesmäki (ICML, 2024)

Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. - Abdullah Akgül; Manuel Haussmann; Melih Kandemir (NeurIPS, 2024)

High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders. - Siddharth Ramchandran; Manuel Haussmann; Harri Lähdesmäki (ICLR, 2025)

Latent mixed-effect models for high-dimensional longitudinal data. - Priscilla Ong; Manuel Haussmann; Otto Lönnroth; Harri Lähdesmäki (Trans. Mach. Learn. Res., 2025)

Improving Plasticity in Non-stationary Reinforcement Learning with Evidential Proximal Policy Optimization. - Abdullah Akgül; Gulcin Baykal; Manuel Haußmann; Melih Kandemir (CoRR, 2025)

Deep Actor-Critics with Tight Risk Certificates. - Bahareh Tasdighi; Manuel Haussmann; Yi-Shan Wu 003; Andrés R. Masegosa; Melih Kandemir (CoRR, 2025)

ObjectRL: An Object-Oriented Reinforcement Learning Codebase. - Gulcin Baykal; Abdullah Akgül; Manuel Haussmann; Bahareh Tasdighi; Nicklas Werge; Yi-Shan Wu 003; Melih Kandemir (CoRR, 2025)