Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Follow Us

Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Follow Us

Publications - page 2

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhman, Walter Karlen,

34th AAAI Conference on Artificial Intelligence (AAAI 2020), 34

Research Collection

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for...

Instance Segmentation for the Quantification of Microplastic Fiber Images

Viktor Wegmayr, Aytunc Sahin, Björn Sæmundsson, Joachim M. Buhmann,

IEEE Winter Conference on Applications of Computer Vision (WACV 2020),

Research Collection

Microplastics pollution has been recognized as a serious environmental concern, with research efforts underway to determine primary causes. Experiments typically generate bright-field images of microplastic fibers that are filtered from water. Environmental decision making in process engineering critically relies on accurate quantification of mi-croplastic fibers in these images. To satisfy the required standards, images are often analyzed manually, resulting in a highly tedious process, with thousands of fiber instances per image. While the shape of individual fibers is relatively simple,...

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Aytunc Sahin, Yatao Bian, Joachim Buhmann, Andreas Krause,

37th International Conference on Machine Learning (ICML 2020) (virtual), 119

DOI: 10.3929/ethz-b-000457122      Research Collection

DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds

Fabian Laumer, Gabriel Fringeli, Alina Dubatovka, Laura Manduchi, Joachim M. Buhmann,

Machine Learning for Health NeurIPS Workshop (ML4H 2020), 136

Research Collection

Echocardiography monitors the heart movement for noninvasive diagnosis of heart diseases. It proves to be of profound practical importance as it combines low-cost portable instrumentation and rapid image acquisition without the risks of ionizing radiation. However, echocardiograms produce high-dimensional, noisy data which frequently proved difficult to interpret. As a solution, we propose a novel autoencoder-based framework, DeepHeartBeat, to learn human interpretable representations of cardiac cycles from cardiac ultrasound data. Our model encodes high dimensional observations by a cyclic trajectory in...

Continuous submodular function maximization

Yatao Bian, Joachim M. Buhmann, Andreas Krause,

arXiv,

DOI: 10.3929/ethz-b-000466476      Research Collection

Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of functions - continuous submodularity - enables both exact minimization and approximate maximization in poly. time. Continuous submodularity is obtained by generalizing the notion of submodularity from discrete domains to continuous domains. It intuitively captures a repulsive effect amongst different dimensions of the defined multivariate function. In this paper, we systematically study continuous submodularity and a class of...

Artificial intelligence in echocardiography diagnostics – detection of takotsubo syndrome

Davide Di Vece, Fabian Laumer, Moritz Schwyzer, Rebekka Burkholz, Luca Corinzia, Victoria L. Cammann, Rodolfo Citro, Jeroen Bax, Jelena R. Ghadri, Joachim M. Buhmann, Christian Templin,

European Society of Cardiology Congress 2020, 41

Research Collection

Unsupervised Mitral Valve Segmentation in Echocardiography with Neural Network Matrix Factorization

Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. Buhmann,

17th Conference on Artificial Intelligence in Medicine in Europe (AIME 2019), 11526

Research Collection

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Đorđe Miladinović, Christine Muheim, Stefan Bauer, Andrea Spinnler, Daniela Noain, Mojtaba Bandarabadi, Benjamin Gallusser, Gabriel Krummenacher, Christian Baumann, Antoine Adamantidis, Steven A. Brown, Joachim M. Buhmann,

PLoS Computational Biology, 15

DOI: 10.3929/ethz-b-000342836      Research Collection

Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) signatures. Traditionally, these states are classified by trained human experts by visual inspection of raw EEG recordings, which is a laborious task prone to inter-individual variability. Recently, machine learning approaches have been developed to automate this process, but their generalization capabilities are often insufficient, especially across animals from...

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference

Yatao Bian, Joachim M. Buhmann, Andreas Krause,

36th International Conference on Machine Learning (ICML 2019), 97

Research Collection

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves-2019

Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen,

arXiv,

Research Collection