Publications [#355657] of Sina Farsiu

Papers Published

1. Hasan, A; Elkhalil, K; Ng, Y; Pereira, JM; Farsiu, S; Blanchet, JH; Tarokh, V, Modeling Extremes with d-max-decreasing Neural Networks, vol. abs/2102.09042 (February, 2021)
   (last updated on 2024/12/31)

   Abstract:
   We propose a novel neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs). MEVs arise from Extreme Value Theory (EVT) as the necessary class of models when extrapolating a distributional fit over large spatial and temporal scales based on data observed in intermediate scales. In turn, EVT dictates that $d$-max-decreasing, a stronger form of convexity, is an essential shape constraint in the characterization of MEVs. As far as we know, our proposed architecture provides the first class of non-parametric estimators for MEVs that preserve these essential shape constraints. We show that our architecture approximates the dependence structure encoded by MEVs at parametric rate. Moreover, we present a new method for sampling high-dimensional MEVs using a generative model. We demonstrate our methodology on a wide range of experimental settings, ranging from environmental sciences to financial mathematics and verify that the structural properties of MEVs are retained compared to existing methods.