Conditional Sparse $\ell_p$-norm Regression With Optimal Probability

no code implementations • 26 Jun 2018 • John Hainline, Brendan Juba, Hai S. Le, David Woodruff

We consider the following conditional linear regression problem: the task is to identify both (i) a $k$-DNF condition $c$ and (ii) a linear rule $f$ such that the probability of $c$ is (approximately) at least some given bound $\mu$, and $f$ minimizes the $\ell_p$ loss of predicting the target $z$ in the distribution of examples conditioned on $c$.

regression