David Saltiel

According to our database, David Saltiel authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?

Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez, Damien Challet

CoRR, 2024

2022

Model-based versus model-free reinforcement learning in quantitative asset management. (Apprentissage par renforcement basé sur un modèle ou sans modèle dans la gestion quantitative des actifs).

David Saltiel

PhD thesis, 2022

2021

Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting.

Eric Benhamou, David Saltiel, Serge Tabachnik, Sui Kai Wong, François Chareyron

CoRR, 2021

Adaptive Supervised Learning for Financial Markets Volatility Targeting Models.

Eric Benhamou, David Saltiel, Serge Tabachnik, Corentin Bourdeix, François Chareyron, Beatrice Guez

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Explainable AI (XAI) Models Applied to the Multi-agent Environment of Financial Markets.

Jean-Jacques Ohana, Steve Ohana, Eric Benhamou, David Saltiel, Beatrice Guez

Proceedings of the Explainable and Transparent AI and Multi-Agent Systems, 2021

2020

Bridging the gap between Markowitz planning and deep reinforcement learning.

Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay

CoRR, 2020

AAMDRL: Augmented Asset Management with Deep Reinforcement Learning.

Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay, Jamal Atif

CoRR, 2020

Time your hedge with Deep Reinforcement Learning.

Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay

CoRR, 2020

Trade Selection with Supervised Learning and Optimal Coordinate Ascent (OCA).

David Saltiel, Eric Benhamou, Rida Laraki, Jamal Atif

Proceedings of the Mining Data for Financial Applications - 5th ECML PKDD Workshop, 2020

Deep Reinforcement Learning (DRL) for Portfolio Allocation.

Eric Benhamou, David Saltiel, Jean-Jacques Ohana, Jamal Atif, Rida Laraki

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning.

Eric Benhamou, David Saltiel, Jean-Jacques Ohana, Jamal Atif

Proceedings of the 25th International Conference on Pattern Recognition, 2020

Bayesian CMA-ES: a new approach.

Eric Benhamou, David Saltiel, Sébastien Vérel

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Similarities between policy gradient methods in reinforcement and supervised learning.

Eric Benhamou, David Saltiel

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

NGO-GM: Natural Gradient Optimization for Graphical Models.

Eric Benhamou, Jamal Atif, Rida Laraki, David Saltiel

CoRR, 2019

BCMA-ES II: revisiting Bayesian CMA-ES.

Eric Benhamou, David Saltiel, Beatrice Guez, Nicolas Paris

CoRR, 2019

BCMA-ES: A Bayesian approach to CMA-ES.

Eric Benhamou, David Saltiel, Sébastien Vérel, Fabien Teytaud

CoRR, 2019

2018

Trade Selection with Supervised Learning and OCA.

David Saltiel, Eric Benhamou

CoRR, 2018

Feature selection with optimal coordinate ascent (OCA).

David Saltiel, Eric Benhamou

CoRR, 2018