About

About the DMML group

The Data Mining and Machine Learning group of Geneva was established in 2011 by Prof. Alexandros Kalousis. It operates as a collaboration between the Department of Information Systems of the University of Applied Sciences, Western Switzerland, Geneva, and the VIPER group of the Computer Science Department of the University of Geneva. Many of the members currently follow a PhD under the joint supervision of Profs. Alexandros Kalousis and Stephane Marchand-Maillet.

We conduct research in various areas of data mining and machine learning, publishing at major international conferences (NeurIPS, ICML, etc.). In our latest work we focus on leveraging the power of modern deep learning architectures to address the problems of generative modeling, continual- and meta- learning, modelling of dynamical systems, and imitation and reinforcement learning. In these we also build on our experiences with metric and kernel learning, feature selection, structured regularization, and many other topics explored over the years by our members.

Many of our research directions have been defined on the basis of real world problems. We collaborate with multiple industrial and academic partners on joint projects providing the necessary machine learning expertise. The group receives funding from different sources, such as the Swiss National Science Foundation, SNSF, InnoSuisse (former CTI), the European Union, Horizon 2020, as well as directly from industrial partners.

News

Archive of news

  • Special session in IPIN2021

    15.4.2021

    We are happy to announce that Greg is organising the Special Session "Data Compression, Data Augmentation and Generative Modeling in Indoor Positioning" in the upcoming Indoor Positioning and Indoor Navigation Conference (IPIN2021).

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  • Kanerva++ at ICLR21

    3.3.2021

    The ICLR21 conference is still a few weeks away but to wet your appetite already, we are glad to let you know that Jason Ramapuram will be presenting there his new paper Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory. The paper is a result of a successful collaboration with Yan ...

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  • PhD Thesis defense of Amina Mollaysa

    23.2.2021

    Amina Mollaysa will defend her PhD Thesis entitled "Structural and Functional Regularization of Deep Learning Models" on Friday 26/02/2021, at 15h00 CET. If you are interested in attending the PhD defence of Amina Mollaysa, please send an email with the title "Thesis defense Mollaysa - Zoom" to alexandros.kalousis@hesge.ch to receive the Zoom link.

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