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In this paper we try to organize machine teaching as a coherent set of ideas.
Relating data compression and learnability
N. Littlestone and M. Warmuth · 1986
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Sample compression, learnability, and the Vapnik-Chervonenkis dimension
S. Floyd and M. K. Warmuth · 1995
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On the complexity of teaching
S. Goldman and M. Kearns · 1995
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Teaching a smarter learner
Sally A. Goldman and H. David Mathias · 1996
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Measuring teachability using variants of the teaching dimension
Frank J. Balbach · 2008
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A survey of robot learning from demonstration
Brenna Argall, Sonia Chernova, Manuela M. Veloso, and Brett Browning · 2009
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning about objects with human teachers
Andrea L Thomaz and Maya Cakmak · 2009
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Adaptive submodularity: Theory and applications in active learning and stochastic optimization
Daniel Golovin and Andreas Krause · 2011
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Models of cooperative teaching and learning
Sandra Zilles, Steffen Lange, Robert Holte, and Martin Zinkevich · 2011
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Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective
Baris Akgun, Maya Cakmak, Jae Wook Yoo, and Andrea Lockerd Thomaz · 2012
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Algorithmic and human teaching of sequential decision tasks
Maya Cakmak and Manuel Lopes · 2012
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Optimizing instructional policies
Robert V Lindsey, Michael C Mozer, William J Huggins, and Harold Pashler · 2013
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On actively teaching the crowd to classify
Adish Singla, Ilija Bogunovic, G Bartók, A Karbasi, and A Krause · 2013
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Machine teaching for Bayesian learners in the exponential family
Xiaojin Zhu · 2013
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Teaching people how to teach robots: The effect of instructional materials and dialog design
Maya Cakmak and Leila Takayama · 2014
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Eliciting good teaching from humans for machine learners
Maya Cakmak and Andrea L Thomaz · 2014
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Recursive teaching dimension, VC-dimension and sample compression
T. Doliwa, G. Fan, H. U. Simon, and S. Zilles · 2014
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Recursive teaching dimension, vc-dimension and sample compression
Thorsten Doliwa, Gaojian Fan, Hans Ulrich Simon, and Sandra Zilles · 2014
Cited alongside, same era.
Modeling the dynamics of classroom education using teaching games
Michael Frank · 2014
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Automatic discovery of cognitive skills to improve the prediction of student learning
Robert V Lindsey, Mohammad Khajah, and Michael C Mozer · 2014
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Improving students’ long-term knowledge retention through personalized review
Robert V Lindsey, Jeffery D Shroyer, Harold Pashler, and Michael C Mozer · 2014
Showing versus doing: Teaching by demonstration
Mark K Ho, Michael Littman, James MacGlashan, Fiery Cushman, and Joseph L Austerweil · 2016
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The teaching dimension of linear learners
Ji Liu and Xiaojin Zhu · 2016
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Faster teaching via pomdp planning
Anna N Rafferty, Emma Brunskill, Thomas L Griffiths, and Patrick Shafto · 2016
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A trainable spaced repetition model for language learning
Burr Settles and Brendan Meeder · 2016
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The label complexity of mixed-initiative classifier training
J. Suh, X. Zhu, and S. Amershi · 2016
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Interactive learning from multiple noisy labels
Shankar Vembu and Sandra Zilles · 2016
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Cited alongside, same era.
Optimal teaching for limited-capacity human learners
K. Patil, X. Zhu, L. Kopec, and B. C. Love · 2014
Cited alongside, same era.
A rational account of pedagogical reasoning: Teaching by, and learning from, examples
Patrick Shafto, Noah D Goodman, and Thomas L Griffiths · 2014
Cited alongside, same era.
Near-optimally teaching the crowd to classify
Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, and Andreas Krause · 2014
Cited alongside, same era.
The security of latent Dirichlet allocation
Shike Mei and Xiaojin Zhu · 2015
Cited alongside, same era.
Using machine teaching to identify optimal training-set attacks on machine learners
Shike Mei and Xiaojin Zhu · 2015
Cited alongside, same era.
Open problem: Recursive teaching dimension versus VC dimension
Hans Ulrich Simon and Sandra Zilles · 2015
Cited alongside, same era.
Explicit defense actions against test-set attacks
Scott Alfeld, Xiaojin Zhu, and Paul Barford · 2017
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Preference-based teaching
Ziyuan Gao, Christoph Ries, Hans U Simon, and Sandra Zilles · 2017
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Quadratic upper bound for recursive teaching dimension of finite VC classes
Lunjia Hu, Ruihan Wu, Tianhong Li, and Liwei Wang · 2017
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Active classification with comparison queries
Daniel M. Kane, Shachar Lovett, Shay Moran, and Jiapeng Zhang · 2017
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Iterative machine teaching
Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, and Le Song · 2017
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Machine teaching: A new paradigm for building machine learning systems
Patrice Y. Simard, Saleema Amershi, David Maxwell Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, and John Wernsing · 2017
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Optimizing Human Learning
B. Tabibian, U. Upadhyay, A. De, A. Zarezade, B. Schoelkopf, and M. Gomez-Rodriguez · 2017
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Approximately optimal teaching of approximately optimal learners
Jacob Whitehill and Javier Movellan · 2017
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No learner left behind: On the complexity of teaching multiple learners simultaneously
Xiaojin Zhu, Ji Liu, and Manuel Lopes · 2017
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Training set debugging using trusted items
Xuezhou Zhang, Xiaojin Zhu, and Stephen Wright · 2018
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