We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise. We demonstrate the effectiveness of our approach by designing algorithms with improved noise tolerance guarantees for learning linear separators. We consider the malicious noise model of Valiant and the adversarial label noise model of Kearns, Schapire, and Sellie. For malicious noise, where the adversary can corrupt an @math of fraction both the label part and the feature part, we provide a polynomial-time algorithm for learning linear separators in @math under the uniform distribution with near information-theoretic optimal noise tolerance of @math . We also get similar improvements for the adversarial label noise model. We obtain similar results for more general classes of distributions including isotropic log-concave distributions. In addition, our algorithms achieve a label complexity whose dependence on the error parameter @math is exponentially better than that of any passive algorithm. This provides the first polynomial-time active learning algorithm for learning linear separators in the presence of adversarial label noise, as well as the first analysis of active learning under the malicious noise model.
 We introduce efficient margin-based algorithms for selective sampling and filtering in binary classification tasks. Experiments on real-world textual data reveal that our algorithms perform significantly better than popular and similarly efficient competitors. Using the so-called Mammen-Tsybakov low noise condition to parametrize the instance distribution, and assuming linear label noise, we show bounds on the convergence rate to the Bayes risk of a weaker adaptive variant of our selective sampler. Our analysis reveals that, excluding logarithmic factors, the average risk of this adaptive sampler converges to the Bayes risk at rate N ?(1+?)(2+?) 2(3+?) where N denotes the number of queried labels, and ?>0 is the exponent in the low noise condition. For all @math this convergence rate is asymptotically faster than the rate N ?(1+?) (2+?) achieved by the fully supervised version of the base selective sampler, which queries all labels. Moreover, for ??? (hard margin condition) the gap between the semi- and fully-supervised rates becomes exponential. We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of our algorithm and on the number of labels it queries when faced with an adaptive adversarial strategy of generating the instances. Our bounds both generalize and strictly improve over previous bounds in similar settings. Additionally, our selective sampling algorithm can be converted into an efficient statistical active learning algorithm. We extend our algorithm and analysis to the multiple-teacher setting, where the algorithm can choose which subset of teachers to query for each label. Finally, we demonstrate the effectiveness of our techniques on a real-world Internet search problem.