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pro vyhledávání: '"Calypso Herrera"'
Publikováno v:
Calypso Herrera
This paper presents new machine learning approaches to approximate the solutions of optimal stopping problems. The key idea of these methods is to use neural networks, where the parameters of the hidden layers are generated randomly and only the last
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60cd7f13a712ea227b3f8a2c38709c85
http://arxiv.org/abs/2104.13669
http://arxiv.org/abs/2104.13669
Publikováno v:
Calypso Herrera
Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d923e55d15b388365636bb92e1be43d
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 34 (1)
Calypso Herrera
Calypso Herrera
This article revisits the problem of decomposing a positive semidefinite matrix as a sum of a matrix with a given rank plus a sparse matrix. An immediate application can be found in portfolio optimization, when the matrix to be decomposed is the cova
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a049f3160d38714ff57062fed27d7ae
http://arxiv.org/abs/1908.00461
http://arxiv.org/abs/1908.00461
Autor:
Calypso Herrera, Louis Paulot
In this paper we introduce a new algorithm for American Monte Carlo that can be used either for American-style options, callable structured products or for computing counterparty credit risk (e.g. CVA or PFE computation). Leveraging least squares reg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4753817a9eacbd0795a5a0e05b7f0889