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Found 9 papers, 2 papers with code
Ergodic optimal liquidations in DeFi
We address the liquidation problem arising from the credit risk management in decentralised finance (DeFi) by formulating it as an ergodic optimal control problem.
Linear convergence of proximal descent schemes on the Wasserstein space
Since the relative entropy is not Wasserstein differentiable, we prove that along the scheme the iterates belong to a certain class of Sobolev regularity, and hence the relative entropy $\operatorname{KL}(\cdot|\pi)$ has a unique Wasserstein sub-gradient, and that the relative Fisher information is indeed finite.
Entropy annealing for policy mirror descent in continuous time and space
We prove that with a fixed entropy level, the mirror descent dynamics converges exponentially to the optimal solution of the regularized problem.
Inefficiency of CFMs: hedging perspective and agent-based simulations
We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk.
Convergence of Policy Gradient for Entropy Regularized MDPs with Neural Network Approximation in the Mean-Field Regime
We show that the objective function is increasing along the gradient flow.
Robust pricing and hedging via neural SDEs
Combining neural networks with risk models based on classical stochastic differential equations (SDEs), we find robust bounds for prices of derivatives and the corresponding hedging strategies while incorporating relevant market data.
Gradient Flows for Regularized Stochastic Control Problems
This paper studies stochastic control problems with the action space taken to be probability measures, with the objective penalised by the relative entropy.
Mean-Field Neural ODEs via Relaxed Optimal Control
We develop a framework for the analysis of deep neural networks and neural ODE models that are trained with stochastic gradient algorithms.
Uniform error estimates for artificial neural network approximations for heat equations
These mathematical results from the scientific literature prove in part that algorithms based on ANNs are capable of overcoming the curse of dimensionality in the numerical approximation of high-dimensional PDEs.
