no code implementations • 19 Apr 2021 • Eric Benhamou, David Saltiel, Serge Tabachnik, Sui Kai Wong, François Chareyron
We also adapt traditional RL methods to real-life situations by considering only past data for the training sets.
no code implementations • 30 Sep 2020 • Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay, Jamal Atif
Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations?
no code implementations • 30 Sep 2020 • Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go.
no code implementations • 16 Sep 2020 • Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay
Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions?
no code implementations • 7 Sep 2020 • Eric Benhamou, David Saltiel, Jean-Jacques Ohana, Jamal Atif
Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving).
no code implementations • 14 May 2019 • Eric Benhamou, Jamal Atif, Rida Laraki, David Saltiel
This paper deals with estimating model parameters in graphical models.
no code implementations • 2 Apr 2019 • Eric Benhamou, David Saltiel, Beatrice Guez, Nicolas Paris
We prove that the expected covariance should be lower in the normal Wishart prior model because of the convexity of the inverse.
no code implementations • 2 Apr 2019 • Eric Benhamou, David Saltiel, Sebastien Verel, Fabien Teytaud
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm.
1 code implementation • 9 Dec 2018 • David Saltiel, Eric Benhamou
We derive this new method using coordinate ascent optimization and using block variables.
1 code implementation • 29 Nov 2018 • David Saltiel, Eric Benhamou
OCA brings substantial differences and improvements compared to previous coordinate ascent feature selection method: we group variables into block and individual variables instead of a binary selection.