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- W3213996813 abstract "We consider sensitivity of a generic stochastic optimization problem to modeluncertainty. We take a non-parametric approach and capture model uncertaintyusing Wasserstein balls around the postulated model. We provide explicitformulae for the first order correction to both the value function and theoptimizer and further extend our results to optimization under linearconstraints. We present applications to statistics, machine learning,mathematical finance and uncertainty quantification. In particular, we provideexplicit first-order approximation for square-root LASSO regressioncoefficients and deduce coefficient shrinkage compared to the ordinary leastsquares regression. We consider robustness of call option pricing and deduce anew Black-Scholes sensitivity, a non-parametric version of the so-called Vega.We also compute sensitivities of optimized certainty equivalents in finance andpropose measures to quantify robustness of neural networks to adversarialexamples." @default.
- W3213996813 created "2021-11-22" @default.
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- W3213996813 date "2021-01-01" @default.
- W3213996813 modified "2023-09-26" @default.
- W3213996813 title "Robust uncertainty sensitivity analysis" @default.
- W3213996813 hasPublicationYear "2021" @default.
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