Statistical Learning Workshop
Workshop by The Data Mining and Machine Learning group of Geneva
In this workshop we bring together the research communities of statistics and machine learning to foster a discussion between the two fields and develop research synergies.
The workshop will take place online the whole day of 18 September. Depending on the COVID-19 status at the time a restricted physical presence version might also take place.
Machine learning can be loosely defined as the set of computational methods that use experience to improve performance or make accurate predictions. With the revival of neural networks and the advent of deep learning, machine learning research now focuses on the development of even more complex models, requiring very large training sets and huge amounts of computational power. Understanding the properties of these models and analyzing them is, at least for the moment, receiving less attention in machine learning research. To fill this gap, one possibility is to rely on one of the pillars of machine learning: statistics.
Many machine learning algorithms rely on statistical models, such as ridge and lasso regression. The statistics community has derived precise theoretical results of these models, including their asymptotic properties or the construction of confidence intervals for parameters. They enable model interpretation, robust inference and causal statements. Such results are mostly still missing in machine learning world, because the models that are used there are inherently complex.
Prof. Alexandros Kalousis from HEG-Genève is one of the organizer of this event
Find out more about this event, full program and registration
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.