Interactive learning is a modern machine learning paradigm of significant practical and  theoretical interest, where the algorithm and the domain expert engage in a two-way dialog to facilitate more accurate learning from less data compared to the classical approach of passively observing labeled data. This workshop will explore several topics related to interactive learning broadly defined, including active learning, in which the learner chooses which examples it wants labeled; explanation-based learning, in which the human doesn't merely tell the machine whether its predictions are right or wrong, but provides reasons in a form that is meaningful to both parties; crowdsourcing, in which labels and other information are solicited from a gallery of amateurs; teaching and learning from demonstrations, in which a party that knows the concept being learned provides helpful examples or demonstrations; and connections and applications to recommender systems, automated tutoring and robotics. Key questions we will explore include what are the right learning models in each case, what are the demands on the learner and the human interlocutor, and what kinds of concepts and other structures can be learned. A main goal of the workshop is to foster connections between theory/algorithms and practice/applications.

Visit the schedule page for archived videos. 

Invited Participants

Jake Abernethy (University of Michigan), Alekh Agarwal (Microsoft Research New York), Anima Anandkumar (UC Irvine), Brenna Argall (Northwestern University), Sanjeev Arora (Princeton University), Pranjal Awasthi (Rutgers University), Nina Balcan (Carnegie Mellon University), Laura Balzano (University of Michigan), Peter Bartlett (UC Berkeley), Misha Belkin (Ohio State University), Shai Ben-David (University of Waterloo), Jeff Bilmes (University of Washington), Joan Bruna (UC Berkeley), Emma Brunskill (Carnegie Mellon University), Kamalika Chaudhuri (UC San Diego), Sonia Chernova (Georgia Institute of Technology), Sanjoy Dasgupta (UC San Diego), Alex Edmonds (University of Toronto), Reza Eghbali (University of Washington), Justin Eldridge (Ohio State University), Maryam Fazel (University of Washington), Dylan Foster (Cornell University), Rong Ge (Duke University), Steve Hanneke, Hamed Hassani (ETH Zurich), Daniel Hsu (Columbia University), Charles Isbell (Georgia Tech), Prateek Jain (Microsoft Research India), Mike Jordan (UC Berkeley), Sham Kakade (University of Washington), Adam Kalai (Microsoft Research New England), Ravi Kannan (Microsoft Research India), Amin Karbasi (Yale University), Andreas Krause (ETH Zurich), Shrinu Kushagra (University of Waterloo), Lihong Li (Microsoft Research), Michael Littman (Brown University), Mike Luby (Qualcomm Technologies, Inc.), Tengyu Ma (Princeton University), Yishay Mansour (Tel Aviv University), Cheng Mao (Massachusetts Institute of Technology), Tom Mitchell (Carnegie Mellon University), Shay Moran (Technion), Robert Murphy (Carnegie Mellon University), Rob Nowak (University of Wisconsin-Madison), Devi Parikh (Georgia Institute of Technology), Luis Rademacher (Ohio State University), Ben Recht (UC Berkeley), Philippe Rigollet (Massachusetts Institute of Technology), Dan Roy (University of Toronto), Karolina Roy (University of Cambridge), Sivan Sabato (Ben-Gurion University), Brian Scassellati (Yale University), John Shawe-Taylor (University College London), Kevin Shi (Columbia University), Aarti Singh (Carnegie Mellon University), Mahdi Soltanolkotabi (University of Southern California), Karthik Sridharan (Cornell University), Xiaorui Sun (Columbia University), Csaba Szepesvari (University of Alberta), Matus Telgarsky (UIUC), Ambuj Tewari (University of Michigan), Andrea Thomaz (University of Texas, Austin), Chris Tosh (UC San Diego), Ruth Urner (Max Planck Institute for Intelligent Systems, Tuebingen), Nicolas Vayatis (ENS Cachan), Yusu Wang (Ohio State University), Xinan Wang (UC San Diego), Manfred Warmuth (UC Santa Cruz), David Woodruff (IBM Research, Almaden), Luke Zettlemoyer (University of Washington), Jerry Zhu (University of Wisconsin-Madison)