Saturday Morning Videos: Interactive Learning workshop, Simons Institute, Berkeley

The Simons Institute at Berkeley sponsored a workshop on Interactive Learning the week before last. The videos are in. Thank you to the organizers Nina Balcan, Emma Brunskill, Robert Nowak , Andrea Thomaz . Here is the introduction to the workshop:

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.

Videos:

• Machine Learning from Verbal User Instruction, Tom Mitchell, Carnegie Mellon University
• Machine Teaching in Interactive Learning, Jerry Zhu, University of Wisconsin-Madison
• Interactively Learning Robot Objective Functions, Anca Dragan, UC Berkeley
• Words, Pictures, and Common Sense, Devi Parikh, Georgia Institute of Technology
• Stochastic Variance Reduction Methods for Policy Evaluation, Lihong Li, Microsoft Research
• Interactive Clustering, Pranjal Awasthi, Rutgers University
• Crowdsourcing and Machine Learning, Adam Kalai, Microsoft Research New England
• Active Learning Beyond Label Feedback, Kamalika Chaudhuri, UC San Diego
• Active Learning for Multidimensional Experimental Spaces of Biological Responses, Robert Murphy, Carnegie Mellon University
• Interactive Language Learning from the Extremes, Sida Wang, Stanford University
• Robot Learning from Motor-Impaired Teachers and Task Partners, Brenna Argall, Northwestern University
• Sample-Efficient Reinforcement Learning with Rich Observations, Alekh Agarwal, Microsoft Research New York
• Robots Learning from Human Interactions, Andrea Thomaz, University of Texas at Austin
• Robot Learning, Interaction and Reliable Autonomy, Sonia Chernova, Georgia Institute of Technology
• Interactive Learning of Parsers from Weak Supervision, Luke Zettlemoyer, University of Washington
• Power of Active Sampling for Unsupervised Learning, Aarti Singh, Carnegie Mellon University
• Leveraging Union of Subspace Structure to Improve Constrained Clustering, Laura Balzano, University of Michigan
• Corralling a Band of Bandit Algorithms, Haipeng Luo, Microsoft Research
• The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits, Csaba Szepesvari, University of Alberta
• RL of Partially Observable Environments Using Spectral Methods, Kamyar Azizzadenesheli, UC Irvine
• Safety in Exploration, Andreas Krause, ETH Zurich
• Optimizing the Spec in RL, Emma Brunskill, Carnegie Mellon University
• I-SED: An Interactive Sound Event Detector, Bongjun Kim, Northwestern University
• Data Dependent Hierarchical Interactive Classification Learning, Shai Ben-David, Univeristy of Waterloo
• Learning with Feature Feedback: From Theory to Practice, Stefanos Poulis, UC San Diego
• Active Nearest Neighbors in Changing Environments, Ruth Urner, Max Planck Institute for Intelligent Systems, Tuebingen
• Interactive Learning Opportunities for the Air Force, Lee Seversky, Air Force Research Laboratory
• Hierarchical Learning for Human-Robot Collaboration, Brian Scassellati, Yale University
• Active Nearest-Neighbor Learning in Metric Spaces, Sivan Sabato, Ben Gurion University
• Systems Presentation, Alekh Agarwal, Microsoft Research New York and Lalit Jain, University of Michigan
• TicToc: A General Technique for Near-Optimal Active Learning with Noise, Steve Hanneke
• Interactive Learning of Mixtures of Submodular Functions, Jeff Bilmes, University of Washington

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