8 papers
Inferring Missing Trajectory Data with Temporal Convolutional Networks
Ilinca Tiriblecea, Gabriel Turinici
Trajectory data collected in real-world settings is frequently incomplete due to sensor failure, communication loss, or occlusion. We address the task of \emph{trajectory inpaintin…
Physics-Informed Neural Networks for coupled stiff transport systems
Laetitia Laguzet, Gabriel Turinici
Purpose: Physics-Informed Neural Networks (PINNs) struggle with stiff, regime-changing transport equations due to instability, loss imbalance, and violations of physical consistenc…
Onflow: a model free, online portfolio allocation algorithm robust to transaction fees
Gabriel Turinici, Pierre Brugiere
We introduce Onflow, a reinforcement learning method for optimizing portfolio allocation via gradient flows. Our approach dynamically adjusts portfolio allocations to maximize expe…
Convergence of a L2 regularized Policy Gradient Algorithm for the Multi Armed Bandit
Stefana Anita, Gabriel Turinici
Although Multi Armed Bandit (MAB) on one hand and the policy gradient approach on the other hand are among the most used frameworks of Reinforcement Learning, the theoretical prope…
Model-Free Deep Hedging with Transaction Costs and Light Data Requirements
Pierre Brugière, Gabriel Turinici
Option pricing theory, such as the Black and Scholes (1973) model, provides an explicit solution to construct a strategy that perfectly hedges an option in a continuous-time settin…
Huber-energy measure quantization
Gabriel Turinici
We describe a measure quantization procedure i.e., an algorithm which finds the best approximation of a target probability law (and more generally signed finite variation measure)…