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

Learn More

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

Learn More

Follow Us

Conference Paper - page 2

2019 Conference Paper

Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs

Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann,

22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), 89

DOI: 10.3929/ethz-b-000385688      Research Collection

2019 Conference Paper

Exact Recovery for a Family of Community-Detection Generative Models

Luca Corinzia, Paolo Penna, Luca Mondada, Joachim M. Buhmann,

IEEE International Symposium on Information Theory (ISIT 2019),

Research Collection

2019 Conference Paper

Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography

Viktor Wegmayr, Giacomo Giuliari, Joachim M. Buhmann,

41st DAGM German Conference on Pattern Recognition (DAGM GCPR 2019), 11824

Research Collection

Diffusion Weighted Mri Probabilistic Tractography Brain Machine Learning Neural Networks
2019 Conference Paper

Disentangled state space models: Unsupervised learning of dynamics across heterogeneous environments

Đorđe Miladinović, Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer,

Deep Generative Models for Highly Structured Data (ICLR 2019 Workshop),

Research Collection

2018 Conference Paper

Scalable variational inference for dynamical systems

Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann,

31st Annual Conference on Neural Information Processing Systems (NIPS 2017), 7

Research Collection

2018 Conference Paper

Non-monotone continuous DR-submodular maximization: structure and algorithms

An Bian, Kfir Y. Levy, Andreas Krause, Joachim M. Buhmann,

31st Annual Conference on Neural Information Processing Systems (NIPS 2017), 1

Research Collection

2018 Conference Paper

Free Energy Asymptotics for Problems with Weak Solution Dependencies

Alexey Gronskiy, Joachim M. Buhmann, Wojciech Szpankowski,

IEEE International Symposium on Information Theory (ISIT 2018),

Research Collection

2018 Conference Paper

Efficient and flexible inference for stochastic systems

Stefan Bauer, Nico S. Gorbach, Djordje Miladinovic, Joachim M. Buhmann,

31st Annual Conference on Neural Information Processing Systems (NIPS 2017), 10

Research Collection

2018 Conference Paper

Data-driven fiber tractography with neural networks

Viktor Wegmayr, Giuliari Giuliar, Stefan Holdener, Joachim M. Buhmann,

IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018),

Research Collection

Tractography Brain Machine Learning Neural Networks
2018 Conference Paper

Classification of Brain MRI with Big Data and deep 3D Convolutional Neural Networks

Viktor Wegmayr, Sai Aitharaju, Joachim M. Buhmann,

Medical Imaging 2018: Computer-Aided Diagnosis, 1057501

Research Collection

Dementia Alzheimer Classification Magnetic Resonance Imaging Machine Learning Deep Learning Convolutional Neural Network