Eric Benhamou

According to our database, Eric Benhamou authored at least 34 papers between 1983 and 2024.

Collaborative distances:

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

2024

Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning.

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

CoRR, 2024

Examining Independence in Ensemble Sentiment Analysis: A Study on the Limits of Large Language Models Using the Condorcet Jury Theorem.

Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, Beatrice Guez, David Saltiel, Thomas Jacquot

CoRR, 2024

Stress index strategy enhanced with financial news sentiment analysis for the equity markets.

Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez, Thomas Jacquot

CoRR, 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

2023

Mathematical study of Peierls instability in the discrete polyacetylene model. (Étude mathématique de l'instabilité de Peierls dans le modèle discret du polyacétylène).

Eric Benhamou

PhD thesis, 2023

FSDA: Tackling Tail-Event Analysis in Imbalanced Time Series Data with Feature Selection and Data Augmentation.

Raphael Krief, Eric Benhamou, Beatrice Guez, Jean-Jacques Ohana, David Saltiel, Rida Laraki, Jamal Atif

Proceedings of the Fifth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2023

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

Estimating Individual Treatment Effects through Causal Populations Identification.

Céline Beji, Eric Benhamou, Michaël Bon, Florian Yger, Jamal Atif

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

2019

Variance Reduction in Actor Critic Methods (ACM).

Eric Benhamou

CoRR, 2019

NGO-GM: Natural Gradient Optimization for Graphical Models.

Eric Benhamou, Jamal Atif, Rida Laraki, David Saltiel

CoRR, 2019

Similarities between policy gradient methods (PGM) in Reinforcement learning (RL) and supervised learning (SL).

Eric Benhamou

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

A discrete version of CMA-ES.

Eric Benhamou, Jamal Atif, Rida Laraki, Anne Auger

CoRR, 2018

A new approach to learning in Dynamic Bayesian Networks (DBNs).

Eric Benhamou, Jamal Atif, Rida Laraki, Anne Auger

CoRR, 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

Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets.

Eric Benhamou

CoRR, 2018

2010

Time Dependent Heston Model.

Eric Benhamou, Emmanuel Gobet, Mohammed Miri

SIAM J. Financial Math., 2010

Assessing the changing U.S. IT R&D ecosystem.

Eric Benhamou, Jon Eisenberg, Randy H. Katz

Commun. ACM, 2010

2009

Smart expansion and fast calibration for jump diffusions.

Eric Benhamou, Emmanuel Gobet, Mohammed Miri

Finance Stochastics, 2009

2000

On the Competition between ECNs, Stock Markets and Market Makers.

Eric Benhamou, Thomas Serval

Proceedings of the Electronic Commerce and Web Technologies, 2000

1984

An Architecture for high Performance Protocol Implementation.

Valerie Lasker, Monte Lien, Eric Benhamou

Proceedings of the Proceedings IEEE INFOCOM 84, San Francisco, CA, USA, April 9-12, 1984, 1984

1983

Multilevel Internetworking Gateways: Architecture and Applications.

Eric Benhamou, Judy Estrin

Computer, 1983