Conditional Sparse ℓ_p-norm Regression With Optimal Probability
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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 μ, and f minimizes the ℓ_p loss of predicting the target z in the distribution of examples conditioned on c. Thus, the task is to identify a portion of the distribution on which a linear rule can provide a good fit. Algorithms for this task are useful in cases where simple, learnable rules only accurately model portions of the distribution. The prior state-of-the-art for such algorithms could only guarantee finding a condition of probability Ω(μ/n^k) when a condition of probability μ exists, and achieved an O(n^k)-approximation to the target loss, where n is the number of Boolean attributes. Here, we give efficient algorithms for solving this task with a condition c that nearly matches the probability of the ideal condition, while also improving the approximation to the target loss. We also give an algorithm for finding a k-DNF reference class for prediction at a given query point, that obtains a sparse regression fit that has loss within O(n^k) of optimal among all sparse regression parameters and sufficiently large k-DNF reference classes containing the query point.
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