Computational Sample Complexity and Attribute-Efficient Learning

Computational Sample Complexity and Attribute-eecient Learning Author to Whom Proofs Should Be Sent

Two fundamental measures of the e ciency of a learning algorithm are its running time and the number of examples it requires (its sample complexity). In this paper we demonstrate that even for simple concept classes, an inherent tradeo can exist between running time and sample complexity. We present a concept class of 1-decision lists and prove that while a computationally unbounded learner can...

The Sample Complexity and Computational Complexity of Boolean Function Learning

This report surveys some key results on the learning of Boolean functions in a probabilistic model that is a generalization of the well-known ‘PAC’ model. A version of this is to appear as a chapter in a book on Boolean functions, but the report itself is relatively self-contained.

Computational Complexity of Attribute Value Grammar

Toward Attribute Efficient Learning Algorithms

We make progress on two important problems regarding attribute efficient learnability. First, we give an algorithm for learning decision lists of length k over n variables using 2 ) logn examples and time n 1/3). This is the first algorithm for learning decision lists that has both subexponential sample complexity and subexponential running time in the relevant parameters. Our approach establis...

Collaborative Clustering: Sample Complexity and Efficient Algorithms

We study the problem of collaborative clustering. This problem is concerned with a set of items grouped into clusters that we wish to recover from ratings provided by users. The latter are also clustered, and each user rates a random but typical small number of items. The observed ratings are random variables whose distributions depend on the item and user clusters only. Unlike for collaborativ...