Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Fat-shattering and the learnability of real-valued functions
Journal of Computer and System Sciences
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
Learnability, Stability and Uniform Convergence
The Journal of Machine Learning Research
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The problem of characterizing learnability is the most basic question of statistical learning theory. A fundamental result is that learnability is equivalent to uniform convergence of the empirical risk to the population risk, and that if a problem is learnable, it is learnable via empirical risk minimization. The equivalence of uniform convergence and learnability was formally established only in the supervised classification and regression setting. We show that in (even slightly) more complex prediction problems learnability does not imply uniform convergence. We discuss several alternative attempts to characterize learnability. This extended abstract summarizes results published in [5, 3].