Explainable Empirical Risk Minimization Alexander Jung, Assistant Professor, Aalto University, Finland Explanation as an essential component of machine-mediated acquisition of knowledge for predictive models Randy G. Goebel, University of Alberta, Canada & XAI-Lab in Edmonton, Alberta, Canada Reinforcement Learning in the Real World: Challenges and Opportunities for Human-Agent Interaction Matthew E. Taylor Director, Intelligent Robot Learning Lab, Associate Professor & Graduate Admissions Chair, Computing Science, Fellow and Fellow-in-Residence, Alberta Machine Intelligence Institute, Canada CIFAR AI Chair, Amii
Almost Matching Exactly Cynthia Rudin, Duke University, US Learning, reasoning, optimisation: Connections, complementarity and chances Holger H. Hoos, Leiden Institute of Advanced Computer Science (LIACS), The Netherlands and University of British Columbia, Canada
Explaining the Decisions of Deep Neural Networks and Beyond Grégoire Montavon, Research Associate TU Berlin, Germany The “sound” of ML implementation. A round trip in the “last mile” and its challenges. Federico Cabitza, PhD, Associate Professor, University of Milano-Bicocca, Italy
Janet Bastiman, Venture Partner at MMC Ventures and Chief Science Officer at StoryStream Yoichi Hayashi, Dept. Computer Science, Meiji University Wojciech Samek, Machine Learning Group at Fraunhofer Heinrich Hertz Institute
The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.