Tuesday, April 07, 2015

Video and Slides: Sketching for M-Estimators: A Unified Approach to Robust Regression, David Woodruff

From the recent Simons Institute event on Information Theory, Learning and Big Data:
We give algorithms for regression for a wide class of M-Estimator loss functions. These generalize l_p-regression to fitness measures used in practice such as the Huber measure, which enjoys the robustness properties of l_1 as well as the smoothness properties of l_2. For such estimators we give the first input sparsity time algorithms. Our techniques are based on the sketch and solve paradigm. The same sketch works for any M-Estimator, so the loss function can be chosen after compressing the data.
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