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Stochastics and Statistics Seminar Alex Wein (University of California, Davis)

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Stochastics and Statistics Seminar Vasilis Syrgkanis (Stanford University)

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Stochastics and Statistics Seminar Vladimir Spokoinyi (Humboldt University of Berlin)

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Fine-Grained Extensions of the Low-Degree Testing Framework

Alex Wein (University of California, Davis)
E18-304

Abstract: The low-degree polynomial framework has emerged as a versatile tool for probing the computational complexity of statistical problems by studying the power and limitations of a restricted class of algorithms: low-degree polynomials. Focusing on the setting of hypothesis testing, I will discuss some extensions of this method that allow us to tackle finer-grained questions…

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Source Condition Double Robust Inference on Functionals of Inverse Problems

Vasilis Syrgkanis (Stanford University)
E18-304

Abstract: We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on the solution to a dual linear inverse problem, where the dual solution can be thought as a generalization of the inverse propensity function. We provide the first source…

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Estimation and inference for error-in-operator model

Vladimir Spokoinyi (Humboldt University of Berlin)
E18-304

Abstract: We consider the Error-in-Operator (EiO) problem of recovering the source x signal from the noise observation Y given by the equation Y = A x + ε in the situation when the operator A is not precisely known. Instead, a pilot estimate \hat{A} is available. The study is motivated by Hoffmann & Reiss (2008), Trabs (2018) and by recent results on high dimensional regression with random design; see e.g., Tsigler, Bartlett (2020) (Benign overfitting…

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