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Poster
Linear regression without correspondence
Daniel Hsu · Kevin Shi · Xiaorui Sun

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #205 #None

This article considers algorithmic and statistical aspects of linear regression when the correspondence between the covariates and the responses is unknown. First, a fully polynomial-time approximation scheme is given for the natural least squares optimization problem in any constant dimension. Next, in an average-case and noise-free setting where the responses exactly correspond to a linear function of i.i.d. draws from a standard multivariate normal distribution, an efficient algorithm based on lattice basis reduction is shown to exactly recover the unknown linear function in arbitrary dimension. Finally, lower bounds on the signal-to-noise ratio are established for approximate recovery of the unknown linear function by any estimator.

Author Information

Daniel Hsu (Columbia University)
Kevin Shi (Columbia University)
Xiaorui Sun (Columbia University)

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