We study random design linear regression with no assumptions on the distribution of covariates and a heavy-tailed response variable. In this distribution-free setting, we show that boundedness conditional second moment given is necessary sufficient condition for achieving nontrivial guarantees. As starting point, prove an optimal version classical in-expectation bound truncated least squares es...

We formulate regression as maximizing the minimum probability (Ω) that the true regression function is within ±2 of the regression model. Our framework starts by posing regression as a binary classification problem, such that a solution to this single classification problem directly solves the original regression problem. Minimax probability machine classification (Lanckriet et al., 2002a) is u...

control of production processes in an industrial environment needs the correct setting of input factors, so that output products with desirable characteristics will be resulted at minimum cost. moreover, such systems havetomeetset of qualitycharacteristicstosatisfycustomer requirements.identifyingthemosteffectivefactorsindesignoftheprocesswhichsupportcontinuousandcontinualimprovement isrecently...