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Thursday, January 24, 2019

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Thomas Gaertner: Interactive Machine Learning

Department Seminar

Interactive Machine Learning
Prof Thomas Gaertner, University of Nottingham

Room CS101, 13:00, Thursday 24/01/19


In this talk I'll give an overview of our contributions to what I call interactive machine learning. Often, interaction in computer science is interpreted as the interaction of humans with the computer but I intend a broader meaning of the interaction of machine learning algorithms with the real world, including but not restricted to humans. Interactions with humans span a broad range where they can be intentional and guided by the human or they can be guided by the computer such that the human is oblivious of the fact that he is being guided. Another example of an interaction with the real world is the use of machine learning algorithms in cyclic discovery processes such as drug design. Important properties of interactive machine learning algorithms include efficiency, effectiveness, responsiveness, and robustness. In the talk I will show how these can be achieved in a variety of interactive contexts.

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Dimap Seminar: Fabian Kuhn
MB2.22

On the role of randomization in local distributed algorithms

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