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COLT
2010
Springer

Learning to Create is as Hard as Learning to Appreciate

11 years 7 months ago
Learning to Create is as Hard as Learning to Appreciate
We explore the relationship between a natural notion of unsupervised learning studied by Kearns et al. (STOC '94), which we call here "learning to create" (LTC), and the standard PAC model of Valiant (CACM '84), which is a form of supervised learning and can be thought of as a formalization of "learning to appreciate". Our main theorem states that "if learning to appreciate is hard, then so is learning to create". That is, we prove that if PAC learning with respect to efficiently samplable input distributions is hard, then solving the LTC problem is also hard. We also investigate ways in which our result are tight.
David Xiao
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where COLT
Authors David Xiao
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