Teachers, learners and black boxes

By Dana Angluin, Martins Krikis. 1997

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Citations in the corpus

Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education (2015, background): “Subsequent theoretical developments can be found in e.g. (Zilles et al. 2011; Balbach and Zeugmann 2009; Angluin 2004; Angluin and Krikis 1997; Goldman and Mathias 1996; Mathias 1997; Balbach and Zeugmann 2006; Balbach 2008; Kobayashi and Shinohara 2009; Angluin and Krikis 2003; Rivest and Yin…”
Teaching Randomized Learners (2006, background): “Angluin and Kriķis’ [3, 4] model prevents collusion by giving incompatible hypothesis spaces to teacher and learner.”
The Teaching Dimension of Linear Learners (2015, background): “ Work Teaching dimension as a learning-theoretic quantity has attracted a long history of research. It was proposed independently in [13, 23]. Subsequent theoretical developments can be found in e.g. [26, 7, 2, 3, 14, 18, 6, 5, 17, 4, 22, 9, 12]. Most of them assume little extra knowledge on the lea”
Teacher Improves Learning by Selecting a Training Subset (2018, background): “The theoretical study of machine teaching has focused on the teaching dimension, i.e. the minimum training set size needed to exactly teach a target concept θ ∗ [20, 38, 46, 18, 29, 45, 16, 44, 48, 9, 3, 4, 21, 31, 8, 7, 25, 5, 36, 23, 10].”
Recent Developments in Algorithmic Teaching (2009, background): “Angluin and Kriķis’ [3, 4] model prevents collusion by giving incompatible hypothesis spaces to teacher and learner.”
Learning from Different Teachers (2003, background): “An earlier version of this paper appeared in the 1997 COLT proceedings (Angluin & Kriķis, 1997).”