Learning from Different Teachers

By Dana Angluin, Martins Krikis. 2003

29 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education (2015, background): “…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 1995; Ben-David and Eiron 1998; Doliwa et al. 2014).”
The Teaching Dimension of Linear Learners (2015, background): “(2011); Balbach & Zeugmann (2009); Angluin (2004); Angluin & Krikis (1997); Goldman & Mathias (1996); Mathias (1997); Balbach & Zeugmann (2006); Balbach (2008); Kobayashi & Shinohara (2009); Angluin & Krikis (2003); Rivest & Yin (1995); Ben-David & Eiron (1998); Doliwa et al.”
Measuring teachability using variants of the teaching dimension (2008, background): “A more explicit teacher is featured in the model by Angluin and Kriķis [2,3].”
Teaching Randomized Learners (2006, background): “Angluin and Kriķis’ [3, 4] model prevents collusion by giving incompatible hypothesis spaces to teacher and learner.”
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.”