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Found 4 papers, 3 papers with code
Neural Optimal Stopping Boundary
A method based on deep artificial neural networks and empirical risk minimization is developed to calculate the boundary separating the stopping and continuation regions in optimal stopping.
Deep Stochastic Optimization in Finance
This paper outlines, and through stylized examples evaluates a novel and highly effective computational technique in quantitative finance.
Deep Empirical Risk Minimization in finance: looking into the future
Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning.
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning.
