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Interactive Learning with Implicit Human Feedback
Andi Peng · Akanksha Saran · Andreea Bobu · Tengyang Xie · Pierre-Yves Oudeyer · Anca Dragan · John Langford

Sat Jul 29 12:00 PM -- 08:00 PM (PDT) @ Meeting Room 315
Event URL: https://interactive-learning-implicit-feedback.github.io/ »

Systems that can learn interactively from their end-users are quickly becoming widespread in real-world applications. Typically humans provide tagged rewards or scalar feedback for such interactive learning systems. However, humans offer a wealth of implicit information (such as multimodal cues in the form of natural language, speech, eye movements, facial expressions, gestures etc.) which interactive learning algorithms can leverage during the process of human-machine interaction to create a grounding for human intent, and thereby better assist end-users. A closed-loop sequential decision-making domain offers unique challenges when learning from humans -– (1) the data distribution may be influenced by the choices of the algorithm itself, and thus interactive ML algorithms need to adaptively learn from human feedback, (2) the nature of the environment itself changes rapidly, (3) humans may express their intent in various forms of feedback amenable to naturalistic real-world settings, going beyond tagged rewards or demonstrations. By organizing this workshop, we attempt to bring together interdisciplinary experts in interactive machine learning, reinforcement learning, human-computer interaction, cognitive science, and robotics to explore and foster discussions on such challenges. We envision that this exchange of ideas within and across disciplines can build new bridges, address some of the most valuable challenges in interactive learning with implicit human feedback, and also provide guidance to young researchers interested in growing their careers in this space.

Author Information

Andi Peng (MIT)
Akanksha Saran (Microsoft Research)
Andreea Bobu (University of California Berkeley)
Tengyang Xie (University of Illinois at Urbana-Champaign)
Pierre-Yves Oudeyer (Inria)

Dr. Pierre-Yves Oudeyer is Research Director (DR1) at Inria and head of the Inria and Ensta-ParisTech FLOWERS team (France). Before, he has been a permanent researcher in Sony Computer Science Laboratory for 8 years (1999-2007). After working on computational models of language evolution, he is now working on developmental and social robotics, focusing on sensorimotor development, language acquisition and life-long learning in robots. Strongly inspired by infant development, the mechanisms he studies include artificial curiosity, intrinsic motivation, the role of morphology in learning motor control, human-robot interfaces, joint attention and joint intentional understanding, and imitation learning. He has published a book, more than 80 papers in international journals and conferences, holds 8 patents, gave several invited keynote lectures in international conferences, and received several prizes for his work in developmental robotics and on the origins of language. In particular, he is laureate of the ERC Starting Grant EXPLORERS. He is editor of the IEEE CIS Newsletter on Autonomous Mental Development, and associate editor of IEEE Transactions on Autonomous Mental Development, Frontiers in Neurorobotics, and of the International Journal of Social Robotics. He is also working actively for the diffusion of science towards the general public, through the writing of popular science articles and participation to radio and TV programs as well as science exhibitions. Web:http://www.pyoudeyer.com and http://flowers.inria.fr

Anca Dragan (University of California, Berkeley)
John Langford (Microsoft Research)

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