Improved Noise-Tolerant Learning and Generalized Statistical Queries
Javed Aslam, Scott E. Decatur · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · 1994
The statistical query learning model can be viewed as a tool for creating (or demonstrating the existence of) noise-tolerant learning algorithms in the PAC model. The complexity of a statistical query algorithm, in conjunction with the complexity of simulating SQ algorithms in the PAC model with noise, determine the complexity of the noise-tolerant PAC algorithms produced. Although roughly optimal upper bounds have been shown for the complexity of statistical query learning, the corresponding noisetolerant PAC algorithms are not optimal due to inefficient simulations. In this paper we provide both improved simulations and a new variant of the statistical query model in order to overcome these inefficiencies. We improve the time complexity of the classification noise simulation of statistical query algorithms. Our new simulation has a roughly optimal dependence on the noise rate. We also derive a simpler proof that statistical queries can be simulated in the presence of classification n...