Teaching Randomized Learners

By Frank J. Balbach, Thomas Zeugmann. 2006

20 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.

1. Xiaojin Zhu
2. Dacheng Tao
3. Ji Liu
4. Chen Gong
5. Wei David Liu
6. Yoshua Bengio
7. Robert D. Nowak
8. Liu Liu

Citations in the corpus

• Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education (2015, background): “…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 1995; Ben-David and Eiron 1998; Doliwa…”
• Machine Teaching for Bayesian Learners in the Exponential Family (2013, background): “The first thread is the teaching dimension theory by Goldman and Kearns [10] and its extensions in computer science(e.g., [1, 2, 11, 12, 14, 25]).”
• Recent Developments in Algorithmic Teaching (2009, background): “The interested reader is referred to [11] for details.”
• How Do Humans Teach: On Curriculum Learning and Teaching Dimension (2011, background): “The first thread, developed by the computational learning theory community, is exemplified by the “teaching dimension” model [9] and its extensions [12, 10, 1, 2, 11, 13, 18].”
• Label Propagation via Teaching-to-Learn and Learning-to-Teach (2017, background): “The early works related to machine teaching mainly focus on the teaching dimension theory [23], [24].”
• 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].”
• Complexity of Teaching by a Restricted Number of Examples (2009, methodology): “Our model is inspired by the study of Balbach and Zeugmann [ BZ06 ].”
• 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”