David Saltiel

According to our database1, David Saltiel authored at least 22 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2024
Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning.
CoRR, 2024

Examining Independence in Ensemble Sentiment Analysis: A Study on the Limits of Large Language Models Using the Condorcet Jury Theorem.
CoRR, 2024

Stress index strategy enhanced with financial news sentiment analysis for the equity markets.
CoRR, 2024

Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?
CoRR, 2024

2023
FSDA: Tackling Tail-Event Analysis in Imbalanced Time Series Data with Feature Selection and Data Augmentation.
Proceedings of the Fifth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2023

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).
PhD thesis, 2022

2021
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting.
CoRR, 2021

Adaptive Supervised Learning for Financial Markets Volatility Targeting Models.
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.
Proceedings of the Explainable and Transparent AI and Multi-Agent Systems, 2021

2020
Bridging the gap between Markowitz planning and deep reinforcement learning.
CoRR, 2020

AAMDRL: Augmented Asset Management with Deep Reinforcement Learning.
CoRR, 2020

Time your hedge with Deep Reinforcement Learning.
CoRR, 2020

Trade Selection with Supervised Learning and Optimal Coordinate Ascent (OCA).
Proceedings of the Mining Data for Financial Applications - 5th ECML PKDD Workshop, 2020

Deep Reinforcement Learning (DRL) for Portfolio Allocation.
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.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Bayesian CMA-ES: a new approach.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Similarities between policy gradient methods in reinforcement and supervised learning.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019
NGO-GM: Natural Gradient Optimization for Graphical Models.
CoRR, 2019

BCMA-ES II: revisiting Bayesian CMA-ES.
CoRR, 2019

BCMA-ES: A Bayesian approach to CMA-ES.
CoRR, 2019

2018
Trade Selection with Supervised Learning and OCA.
CoRR, 2018

Feature selection with optimal coordinate ascent (OCA).
CoRR, 2018


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