Emerging areas in Stochastic Simulation

A new likelihood ratio method for training artificial neural networks

Talk - Option Pricing by Neural Stochastic Differential Equations: A Simulation Optimization Approach

Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving

Large-scale inventory optimization: A recurrent-neural-networks-inspired simulation approach

Robust simulation with likelihood-ratio constrained input uncertainty

Learning-based robust optimization procedures and statistical guarantee

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 …

Ranking and selection with covariates for personalized decision making

Surrogate-based simulation optimization

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