no code implementations • 8 Feb 2024 • Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process.
1 code implementation • 6 Nov 2023 • Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi V. Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki
Compared to previous methods, our results show improved performance both for direct treatment effect estimation as well as for effect estimation via patient matching.
2 code implementations • ICLR 2022 • Melih Kandemir, Abdullah Akgül, Manuel Haussmann, Gozde Unal
A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e. g.\ class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them.
no code implementations • 17 Jun 2020 • Manuel Haussmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
1 code implementation • 27 Jun 2019 • Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
As active learning is a scarce data regime, we bootstrap from a well-known heuristic that filters the bulk of data points on which all heuristics would agree, and learn a policy to warp the top portion of this ranking in the most beneficial way for the character of a specific data distribution.
1 code implementation • pproximateinference AABI Symposium 2021 • Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
We propose a novel method for closed-form predictive distribution modeling with neural nets.
1 code implementation • 19 May 2018 • Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation.
1 code implementation • CVPR 2017 • Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
Gaussian Processes (GPs) are effective Bayesian predictors.