Recursive teaching dimension, VC-dimension and sample compression

By Thorsten Doliwa, Gaojian Fan, H. Simon, Sandra Zilles. 2014

81 citations recorded by the source corpus.

How researchers see this paper

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

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).”
An Overview of Machine Teaching (2018, background): “This is to be contrasted with the VC dimension of d-dimensional hyperplanes, which is VCD = d+ 1.”
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications (2018, background): “Research in computational learning theory has shown a dramatic reduction in the number of teaching examples needed to teach anticipatory learners who know they are being taught by a teacher (Doliwa et al. 2014; Gao et al. 2017), but has not addressed sequential decision making tasks.”
The Teaching Dimension of Linear Learners (2015, unknown): “L G ] 7 D ec”
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].”