We survey the active interface of statistical learning methods and quantitative finance
models. Our focus is on the use of statistical surrogates, also known as functional …

This paper develops a new neural network architecture for modeling spatial distributions (ie
distributions on R d) which is more computationally efficient than a traditional fully …

In this paper we propose two efficient techniques which allow one to compute the price of
American basket options. In particular, we consider a basket of assets that follow a multi …

Due to the lack of reliable market information, building financial term-structures may be
associated with a significant degree of uncertainty. In this paper, we propose a new term …

An efficient compression technique based on hierarchical tensors for popular option pricing
methods is presented. It is shown that the “curse of dimensionality” can be alleviated for the …

In this paper we propose an efficient method to compute the price of multi-asset American
options, based on Machine Learning, Monte Carlo simulations and variance reduction …

Modeling counterparty risk is computationally challenging because it requires the
simultaneous evaluation of all the trades with each counterparty under both market and …

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We develop a Gaussian process (GP) framework for modeling mortality rates and mortality
improvement factors. GP regression is a nonparametric, data-driven approach for …

We consider the problem of learning the level set for which a noisy black-box function
exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian …