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Option Pricing By Neural Stochastic Differential Equations: A Simulation-optimization Approach

Classical option pricing models rely on prior assumptions made on the dynamics of the underlying assets and the rationality of the market. While empirical evidence showed that these models may explain the option prices to certain extend, their …

A novel learning framework for sampling-based motion planning in autonomous driving

Training Artificial Neural Networks by Generalized Likelihood Ratio Method: An Effective Way to Improve Robustness

Estimating sensitivity to input model variance

Fully sequential ranking-and-selection procedures with PAC guarantee

Gaussian Mixture Model-based Random Search for Continuous Optimization via Simulation

A new framework of designing sequential ranking-and-selection procedures

Ranking and selection with covariates

A simulation analytics approach to dynamic risk monitoring

Approximating data-driven joint chance constrained programs via uncertainty set construction