[HDI Lab seminar] Distribution-Free Robust Linear Regression

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We consider the problem of random-design linear regression, in a distribution-free setting where no assumption is made on the distribution of the predictive/input variables. After surveying existing approaches and indicating some improvements, we explain why they fall short in our setting. We then identify the minimal assumption on the target/output under which guarantees are possible and describe a nonlinear prediction procedure achieving the optimal error bound with high probability. Joint work with Jaouad Mourtada (CREST-ENSAE Paris ) and Tomas Vaškevičius (Oxford).

Speaker: Nikita Zhivotovskiy, ETH (Zurich).

March 16, 2021

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