Thursday, September 26, 2013 - 3:05pm
1 hour (actually 50 minutes)
We analyze active learning algorithms, which only receive the classifications of examples when they ask for them, and traditional passive (PAC) learning algorithms, which receive classifications for all training examples, under log-concave and nearly log-concave distributions. By using an aggressive localization argument, we prove that active learning provides an exponential improvement over passive learning when learning homogeneous linear separators in these settings. Building on this, we then provide a computationally efficient algorithm with optimal sample complexity for passive learning in such settings. This provides the first bound for a polynomial-time algorithm that is tight for an interesting infinite class of hypothesis functions under a general class of data-distributions, and also characterizes the distribution-specific sample complexity for each distribution in the class. We also illustrate the power of localization for efficiently learning linear separators in two challenging noise models (malicious noise and agnostic setting) where we provide efficient algorithms with significantly better noise tolerance than previously known.