Journal of Machine Learning Research x (2011) xx-xx Submitted 6/10; Revised 12/10; Published x/11 Models of Cooperative Teaching and Learning

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…”
• Machine Teaching for Bayesian Learners in the Exponential Family (2013, background): “Our framework allows for probabilistic, noisy learners with infinite hypothesis space, arbitrary loss functions, and the notion of teaching effort.”
• 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].”
• Recursive teaching dimension, VC-dimension and sample compression (2014, background): “Definition 2 (Zilles et al. (2011)) A teaching plan for C is a sequence P = ((C1, S1), . . . , (CN , SN )) (2) with the following properties: • N = |C| and C = {C1, . . . , CN}.”
• 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].”
• 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”
• 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].”
• Interpretable and Pedagogical Examples (2017, background): “These additional constraints include requiring the student to be able to learn through any superset of the teacher’s examples (Goldman & Mathias, 1996), requiring the learned protocols to work for any ordering of the concepts or examples (Zilles et al., 2011), requiring the student to learn all conc”
• An Overview of Machine Teaching (2018, background): “There is no generally adopted notion of collusion, see [19, 50] for some examples.”
• Teaching a black-box learner (2019, background): “Earlier theoretical work on teaching assumes both, such as the classic teaching dimension (Goldman & Kearns, 1995; Shinohara & Miyano, 1991), the recursive teaching dimen-sion (Zilles et al., 2011; Hu et al., 2017) and the preference-based teaching dimension (Gao et al., 2017).”