Measuring teachability using variants of the teaching dimension

By Frank J. Balbach. 2008

53 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.

In Artificial Intelligence

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).”
Recursive teaching dimension, VC-dimension and sample compression (2014, background): “…dimension—the measure of information complexity in the “classical” teaching model (Goldman and Kearns, 1995; Shinohara and Miyano, 1991)— but also the information complexity of iterated optimal teaching (Balbach, 2008), which is often substantially smaller than the classical teaching dimension.”
The Teaching Dimension of Linear Learners (2015, background): “Balbach (2008); Zilles et al. (2011)), it does not capture all natural teaching and learning behaviors.”
Recent Developments in Algorithmic Teaching (2009, background): “One such model (see Balbach [7, 8]) assumes the learner picks hypotheses that are not only consistent but of minimal complexity.”
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].”
Mixed-Initiative Active Learning (2012, background): “A number of teachability metrics have been proposed (Natarajan, 1989; Anthony et al., 1995; Balbach, 2008), the most popular being the Teaching Dimension (Goldman & Kearns, 1995).”
Algorithmic and Human Teaching of Sequential Decision Tasks (2012, background): “The Algorithmic Teaching literature presents a range of metrics that characterize the teachability of a concept, such as the Teaching Dimension (Goldman and Kearns 1995), or its variants (Natarajan 1987; Anthony, Brightwell, and Shawe-Taylor 1995; Balbach 2008).”
Toward a general, scaleable framework for Bayesian teaching with applications to topic models (2016, background): “…proposed and investigated probabilistic models of optimal example selection [4–7]; algorithmic teaching researchers have proposed deterministic methods to select examples that rule out confusable concepts [8, 9]; and machine learning researchers have become interested in machine teaching [10, 11].”
An Overview of Machine Teaching (2018, background): “owever,anincreasingnumberofteachingsettingsnowinvolveastudentwhoisawarethatitis beingtaughtbyateacher. Incomputationallearningtheory,suchanticipatorystudentenablesvariousmodelsofteaching, see, e.g., [5,12,16,50]. For example, the notions of Recursive Teaching Dimension (RTD) andPreference-basedTeach”