=Paper=
{{Paper
|id=Vol-2738/keynote3
|storemode=property
|title=Interactive Machine Learning with Structured Data
|pdfUrl=https://ceur-ws.org/Vol-2738/keynote3.pdf
|volume=Vol-2738
|authors=Thomas Gärtner
|dblpUrl=https://dblp.org/rec/conf/lwa/000120
}}
==Interactive Machine Learning with Structured Data==
<pdf width="1500px">https://ceur-ws.org/Vol-2738/keynote3.pdf</pdf>
<pre>
Interactive Machine Learning with Structured
                    Data

                                Thomas Gärtner

            Faculty of Informatics, TU Wien, 1040 Vienna, Austria
                        thomas.gaertner@tuwien.ac.at



    Abstract. 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.




Copyright ©   2020 by the paper’s authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).

</pre>