We consider the problems of attribute-efficient PAC learning of two well-studied concept classes: parity functions and DNF expressions over $\{0,1\}^n$. We show that attribute-efficient learning of parities with respect to the uniform distribution is equivalent to decoding high-rate random linear codes from low number of errors, a long-standing open problem ... more >>>

Detecting the relevant attributes of an unknown target concept
is an important and well studied problem in algorithmic learning.
Simple greedy strategies have been proposed that seem to perform reasonably
well in practice if a sufficiently large random subset of examples of the target
concept is provided.

Introducing a ... more >>>

In this note we present an approximation algorithm for MAX 2SAT that given a (1 - epsilon) satisfiable instance finds an assignment of variables satisfying a 1 - O(sqrt{epsilon}) fraction of all constraints. This result is optimal assuming the Unique Games Conjecture.

The best previously known result, due ... more >>>