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Deconfounding using Spectral Transformations

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STSW04 - Future challenges in statistical scalability

High-dimensional regression methods which rely on the sparsity of the ground truth, such as the Lasso, might break down in the presence of confounding variables. If a latent variable affects both the response and the predictors, the correlation between them changes. This phenomenon can be represented as a linear model where the sparse coefficient vector has been perturbed. We will present our work on this problem. We investigate and propose some spectral transformations for the data which serve as input for the Lasso. We discuss assumptions for achieving the optimal error rate and illustrate the performance on a genomic dataset. The approach is easy to use and leads to convincing results. The talk is based on joint work with Nicolai Meinshausen.

This talk is part of the Isaac Newton Institute Seminar Series series.

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