1 citations · 2 across the 3 of their papers we have counts for
7 papers · 1 filter
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting
Eric Benhamou, David Saltiel, Serge Tabachnik +2
Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial mar…
Bridging the gap between Markowitz planning and deep reinforcement learning
Eric Benhamou, David Saltiel, Sandrine Ungari +1
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum vari…
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning
Eric Benhamou, David Saltiel, Sandrine Ungari +2
Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how De…
NGO-GM: Natural Gradient Optimization for Graphical Models
Eric Benhamou, Jamal Atif, Rida Laraki +1
This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent s…
BCMA-ES II: revisiting Bayesian CMA-ES
Eric Benhamou, David Saltiel, Beatrice Guez +1
This paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computat…
BCMA-ES: A Bayesian approach to CMA-ES
Eric Benhamou, David Saltiel, Sebastien Verel +1
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum fol…