7 papers
From Arbitrage Removal to Density Extraction: A Model-Free Framework for Short-Dated Options
Aaron Wizman, Gabriel Turinici, Gregory Merran
We study risk-neutral density extraction from short-dated option chains. As expiry approaches, option premia decline and bid--ask spreads can be large relative to prices, making mi…
Vanishing L2 regularization for the softmax Multi Armed Bandit
Stefana-Lucia Anita, Gabriel Turinici
Multi Armed Bandit (MAB) algorithms are a cornerstone of reinforcement learning and have been studied both theoretically and numerically. One of the most commonly used implementati…
Softmax gradient policy for variance minimization and risk-averse multi armed bandits
Gabriel Turinici
Algorithms for the Multi-Armed Bandit (MAB) problem play a central role in sequential decision-making and have been extensively explored both theoretically and numerically. While m…
The impact of recovery rate heterogeneity in achieving herd immunity
Gabriel Turinici
Herd immunity is a critical concept in epidemiology, describing a threshold at which a sufficient proportion of a population is immune, either through infection or vaccination, the…
Regime-Aware Time Weighting for Physics-Informed Neural Networks
Gabriel Turinici
We introduce a novel method to handle the time dimension when Physics-Informed Neural Networks (PINNs) are used to solve time-dependent differential equations; our proposal focuses…
Optimal time sampling in physics-informed neural networks
Gabriel Turinici
Physics-informed neural networks (PINN) is a extremely powerful paradigm used to solve equations encountered in scientific computing applications. An important part of the procedur…