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Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling
This video was recorded at 26th Annual Conference on Learning Theory (COLT), Princeton 2013. We propose a new method for estimating the locations and the value of an absolute maximum (minimum) of a function from the observations contaminated by random noise. Our goal is to solve the problem under minimal regularity and shape constraints. In particular, we do not assume differentiability of a function nor that its maximum is attained at a single point. We provide tight upper and lower bounds for the performance of proposed estimators. Our method is adaptive with respect to the unknown parameters of the problem over a large class of underlying distributions.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
