Recent Developments in Algorithmic Teaching

By Frank J. Balbach, Thomas Zeugmann. 2009

55 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

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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…”
Cooperative Inverse Reinforcement Learning (2016, background): “Because CIRL incentivizes the human to teach, as opposed to maximizing reward in isolation, our work is related to optimal teaching: ﬁnding examples that optimally train a learner (Balbach & Zeugmann, 2009; Goldman et al., 1993; Goldman & Kearns, 1995).”
Becoming the expert - interactive multi-class machine teaching (2015, background): “Other theoretically motivated works, while interesting, provide little validation on real human subjects [3, 13, 46].”
How Do Humans Teach: On Curriculum Learning and Teaching Dimension (2011, methodology): “Finally, we provide a novel theoretical analysis that extends recent ideas in teaching dimension model [13, 3] and offers curriculum learning a rigorous underpinning.”
Mixed-Initiative Active Learning (2012, background): “Evidence supporting this comes from the field of Algorithmic Teaching [?, ?, 4].”
Algorithmic and Human Teaching of Sequential Decision Tasks (2012, background): “Work within AT, tries to identify the teachability of different concept classes and devise efficient algorithms that produce optimal teaching sequences (Balbach and Zeugmann 2009; Goldman and Kearns 1995).”
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications (2018, background): “formative demonstrations from a teacher. 2 Related work Determining the minimum number of demonstrations needed to teach a task falls under the ﬁelds of Algorithmic Teaching (Goldman and Kearns 1995; Balbach and Zeugmann 2009) and Machine Teaching (Zhu 2015; Zhu et al. 2018). However, almost all pre”
Online Transfer Learning in Reinforcement Learning Domains (2015, background): “Finally, a branch in computational learning theory called algorithmic teaching tries to understand teaching in theoretical ways Balbach and Zeugmann (2009).”
Optimal Teaching for Limited-Capacity Human Learners (2014, background): “Such a result would establish a conceptual link between idealization manipulations from psychology and optimal teaching procedures from machine learning [7, 6, 8, 2, 9, 10, 11].”
Multi-Armed Bandits for Intelligent Tutoring Systems (2013, background): “Work within AT tries to identify the teachability of different concept classes and devise efficient algorithms that produce optimal teaching sequences [5, 29, 15].”
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”
The strategic student approach for life-long exploration and learning (2012, background): “optimal teaching techniques [1], [2] or in curriculum learning [3], [4].”
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].”