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.

email  /  dplp  /  scholar  /  github

Research

A sampling-free approach to BNN learning via ReLU decomposition.

We introduce a way to adaptively learn the acquisition function for active learning with reinforcement feedback.

We present LeMoNADe, an end-to-end learned motif detection method directly operating on calcium imaging videos.

Instance level inference with closed-form updates in a Gaussian processed-based model for weakly supervised multiple instance learning and object detection.

We introduce a model for multiple instance learning with Gaussian processes for bag-level predictions with closed-form updates.

Design and source code based on Jon Barron's website.