This is a talk presented at Nanmat conference held Nov 4-7 2024 at ICTP, Cluj.
Talk materials: 
the slides of the presentation. and here the 
Youtube video version.
Auteur : gabriel
General chair of the conference FAAI24 « Foundations and applications of artificial intelligence », Iasi, October 28-30, 2024
General chair with C. Lefter and A. Zalinescu of the conference FAAI24 « Foundations and applications of artificial intelligence » Iasi Oct 28-30 2024. At the conference I also serve as tutorial presenter.
LLM and time series at the « 6th J.P. Morgan Global Machine Learning Conference », Paris, Oct 18th, 2024
Invited joint talk « Using LLMs techniques for time series prediction » with Pierre Brugiere presented at the 6th JP Morgan Global Machine Learning conference held in Paris, Oct 18th 2024
Talk materials: slides(click here) and here a link to the associated paper.
Co-organizer of the « Numerical Analysis » sessions at « Le 16ème Colloque Franco-Roumain, Bucarest, Roumanie »
Co-organizer with E. Catinas of the « Numerical Analysis » sessions at « Le 16ème Colloque Franco-Roumain, Bucarest, Roumanie »
Interview with radio « France Culture » on the ethics of generative AI
A short interview with Celine Loozen from ‘France Culture’ radio station within a radio program concerning AI and GAFAM ethics.
Link for the full radio broadcast
Interview with Celine Loozen : here (local version if necessary here)
Reinforcement Learning, M2 ISF App, 2021-2025
Instructor: Gabriel TURINICI
1/ Introduction to reinforcement learning
2/ Theoretical formalism: Markov decision processes (MDP), value function ( Belman and Hamilton- Jacobi – Bellman equations) etc.
3/ Common strategies, building from the example of « multi-armed bandit »
4/ Strategies in deep learning: Q-learning and DQN
5/ Strategies in deep learning: SARSA and variants
6/ Strategies in deep learning: Actor-Critic and variants
7/ During the course: various Python and gym/gymnasium implementations
8/ Perspectives.
Multi Armed Bandit codes (MAB) : play MAB, solve MAB , solve MAB v2., policy grad from chatGPT to correct., policy grad corrected.
Bellman iterations: code to correct here, solution code here
Gym: play Frozen Lake (v2023) (version 2022)
Q-Learning : with Frozen Lake, python version or notebook version
-play with gym/Atari-Breakout: python version or notebook version
Deep Q Learning (DQN) : Learn with gym/Atari-Breakout: notebook 2024 and its version with smaller NN and play with result
Policy gradients on Pong adapted from Karpathy, 2024 version (correct to get it working!) python or notebook
You can also load from HERE a converged version (rename as necessary) pg_pong_converged_turinici24
Notebook to use it: here (please send me yours if mean reward above 15!).
Projets : cf. Teams
Round table at the Dauphine Digital Days
Intervention at the round table « AI and medias: when everything is accelerating » at the Dauphine Digital Days held Nov 20-22 2023 at the Université Paris Dauphine – PSL, Paris, France.
Video version: from minute 09:00
Conference badge 🙂 
« Reinforcement learning in finance: online portfolio allocation and policy gradient approaches, the Onflow algorithm », NANMATH nov 2023
This is a talk presented at Nanmath conference held Nov 6-9 2023 at ICTP, Cluj..
Talk materials (click to open or download): the Slides of the presentation, the ArXiv preprint and the 
Youtube VIDEO.
« Reinforcement learning in finance: portfolio allocation, value functions and policy gradients flows », ACDSDE conference sept 2023
This is a talk presented at ACDSDE conference held Sept 28-30 2023 at the Romanian Academy (Iasi station), Octav Mayer Institute of mathematics.
Talk materials: the slides of the presentation.
« Deep conditional measure quantization », OL2A 2023 conference
This is a talk presented at OL2A: « International Conference on Optimization, Learning Algorithms and Applications 2023 », September, 27 – 29, 2023, Ponta Delgada (Portugal) 
Talk materials: the slides of the presentation.