AAAI-17 and Microsoft Research

AAAI is pleased to present the AAAI-17 Workshop Program. Workshops will be held Sunday and Monday, February 4-5, 2017 at the Hilton San Francisco Union Square in San Francisco, California, USA. Exact locations and dates for the workshops will be determined in January. The AAAI-17 workshop program includes 17 workshops covering a wide range of topics in artificial intelligence. Workshops are one day unless noted otherwise in the individual description. Participation in each workshop is generally limited to 25-65 participants, and participation is usually by invitation from the workshop organizers. However, most workshops also allow general registration by other interested individuals. Please note that there is a separate registration fee for attendance at a workshop. Workshop registration is available for workshop only registrants or for AAAI-17 technical registrants at a discounted rate. Registration information will be mailed directly to all invited participants. Workshop papers, if included in the AAAI Technical Report Series, will be made available to all registered attendees, as well as after the conference as part of the AAAI Digital Library.

Scientific Committee members

• Michael Leavit
• Ece Kamar
• Michael Tjalve

Organizing Committee members

• Distributed Machine Learning Workshop, organized by Tie-Yan Liu, et. al.

Tutorials

• Recent Advances in Distributed Machine Learning, by Tie-Yan Liu, Wei Chen, and Taifeng Wang
• Interactive Machine Learning: From Classifiers to Robotics, by Brad Hayes, Ece Kamar, and Matthew E. Taylor

Accepted papers

• “Long-Term Trends in the Public Perception of Artificial Intelligence” by Ethan Fast and Eric Horvitz
• “On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems” by Besmira Nushi, Ece Kamar, Eric Horvitz and Donald Kossmann
• “Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration” by Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Eric Horvitz

Student Abstracts

• “Predicting ICU Mortality via Learning about Hazard” by Dae Hyun Lee and Eric Horvitz

Emerging papers

• “Computing Safer Driving Routes” by John Krumm and Eric Horvitz