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| Publications [#369085] of Sina Farsiu
Papers Published
- Hasan, A; Elkhalil, K; Ng, Y; Pereira, JM; Farsiu, S; Blanchet, J; Tarokh, V, Modeling Extremes with d-max-decreasing Neural Networks,
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022
(January, 2022),
pp. 759-768, ISBN 9781713863298
(last updated on 2024/12/31)
Abstract:
We propose a 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 the 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.
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