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Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user.
Explanation-based generalization: A unifying view
Tom M Mitchell, Richard M Keller, and Smadar T Kedar-Cabelli · 1986
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Trust and intergroup negotiation
Roderick M Kramer and Peter J Carnevale · 2001
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Toward optimal active learning through monte carlo estimation of error reduction
Nicholas Roy and Andrew McCallum · 2001
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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1-norm support vector machines
Ji Zhu, Saharon Rosset, Robert Tibshirani, and Trevor J Hastie · 2004
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Active learning with feedback on features and instances
Hema Raghavan, Omid Madani, and Rosie Jones · 2006
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Upper and lower error bounds for active learning
Rui M Castro and Robert D Nowak · 2006
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Psychological foundations of trust
Jeffry A Simpson · 2007
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Probabilistic explanation based learning
Angelika Kimmig, Luc De Raedt, and Hannu Toivonen · 2007
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An interactive algorithm for asking and incorporating feature feedback into support vector machines
Hema Raghavan and James Allan · 2007
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
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Nonmyopic active learning of gaussian processes: an exploration-exploitation approach
Andreas Krause and Carlos Guestrin · 2007
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Preferences in interactive systems: Technical challenges and case studies
Bart Peintner, Paolo Viappiani, and Neil Yorke-Smith · 2008
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Learning from labeled features using generalized expectation criteria
Gregory Druck, Gideon Mann, and Andrew McCallum · 2008
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Modeling annotators: A generative approach to learning from annotator rationales
Omar F Zaidan and Jason Eisner · 2008
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Active learning by labeling features
Gregory Druck, Burr Settles, and Andrew McCallum · 2009
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Interacting meaningfully with machine learning systems: Three experiments
Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret Burnett, Thomas Dietterich, Erin Sullivan, and Jonathan Herlocker · 2009
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Seeing is believing: Trustworthiness as a dynamic belief
Luke J Chang, Bradley B Doll, Mascha van’t Wout, Michael J Frank, and Alan G Sanfey · 2010
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A unified approach to active dual supervision for labeling features and examples
Josh Attenberg, Prem Melville, and Foster Provost · 2010
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The true sample complexity of active learning
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman Vaughan · 2010
Impact of robot failures and feedback on real-time trust
Munjal Desai, Poornima Kaniarasu, Mikhail Medvedev, Aaron Steinfeld, and Holly Yanco · 2013
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The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle
Adam Waytz, Joy Heafner, and Nicholas Epley · 2014
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Eliciting good teaching from humans for machine learners
Maya Cakmak and Andrea L Thomaz · 2014
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Theory of disagreement-based active learning
Steve Hanneke et al · 2014
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Coactive learning
Pannaga Shivaswamy and Thorsten Joachims · 2015
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Principles of explanatory debugging to personalize interactive machine learning
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf · 2015
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Why do humans reason? arguments for an argumentative theory
Hugo Mercier and Dan Sperber · 2011
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Explanation-based learning
Gerald DeJong and Shiau Hong Lim · 2011
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Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances
Burr Settles · 2011
Cited alongside, same era.
Mixed-initiative active learning
Maya Cakmak and Andrea L Thomaz · 2011
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The constrained weight space svm: learning with ranked features
Kevin Small, Byron C Wallace, Carla E Brodley, and Thomas A Trikalinos · 2011
Cited alongside, same era.
‘I won’t trust you if I think you’re trying to deceive me’: Relations between selective trust, theory of mind, and imitation in early childhood
Cara DiYanni, Deniela Nini, Whitney Rheel, and Alicia Livelli · 2012
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Active learning with rationales for text classification
Manali Sharma, Di Zhuang, and Mustafa Bilgic · 2015
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Trust calibration within a human-robot team: Comparing automatically generated explanations
Ning Wang, David V Pynadath, and Susan G Hill · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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An unexpected unity among methods for interpreting model predictions
Scott Lundberg and Su-In Lee · 2016
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Preferences in artificial intelligence
Gabriella Pigozzi, Alexis Tsoukias, and Paolo Viappiani · 2016
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Right for the right reasons: training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Interpreting blackbox models via model extraction
Osbert Bastani, Carolyn Kim, and Hamsa Bastani · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Short-term satisfaction and long-term coverage: Understanding how users tolerate algorithmic exploration
Tobias Schnabel, Paul N Bennett, Susan T Dumais, and Thorsten Joachims · 2018
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