Formal models of learning from teachers need to respect certain criteria to avoid collusion. The most commonly accepted notion of collusion-freeness was proposed by Goldman and Mathias (1996), and various teaching models obeying their criterion have been studied. For each model $M$ and each concept class $mathcal{C}$, a parameter $M$-$mathrm{TD}(mathcal{C})$ refers to the teaching dimension of concept class $mathcal{C}$ in model $M$---defined to be the number of examples required for teaching a concept, in the worst case over all concepts in $mathcal{C}$. This paper introduces a new model of teaching, called no-clash teaching, together with the corresponding parameter $mathrm{NCTD}(mathcal{C})$. No-clash teaching is provably optimal in the strong sense that, given any concept class $mathcal{C}$ and any model $M$ obeying Goldman and Mathiass collusion-freeness criterion, one obtains $mathrm{NCTD}(mathcal{C})le M$-$mathrm{TD}(mathcal{C})$. We also study a corresponding notion $mathrm{NCTD}^+$ for the case of learning from positive data only, establish useful bounds on $mathrm{NCTD}$ and $mathrm{NCTD}^+$, and discuss relations of these parameters to the VC-dimension and to sample compression. In addition to formulating an optimal model of collusion-free teaching, our main results are on the computational complexity of deciding whether $mathrm{NCTD}^+(mathcal{C})=k$ (or $mathrm{NCTD}(mathcal{C})=k$) for given $mathcal{C}$ and $k$. We show some such decision problems to be equivalent to the existence question for certain constrained matchings in bipartite graphs. Our NP-hardness results for the latter are of independent interest in the study of constrained graph matchings.
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