2 citations · 5 across the 11 of their papers we have counts for
11 papers
From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation
Abdelhakim Benechehab, Gabriel Singer, Corentin Léger +5
Generative models form the backbone of modern machine learning, underpinning state-of-the-art systems in text, vision, and multimodal applications. While Maximum Likelihood Estimat…
LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection
Youssef Attia El Hili, Albert Thomas, Malik Tiomoko +4
Model and hyperparameter selection are critical but challenging in machine learning, typically requiring expert intuition or expensive automated search. We investigate whether larg…
TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
Giuseppe Paolo, Abdelhakim Benechehab, Hamza Cherkaoui +2
Hierarchical organization is fundamental to biological systems and human societies, yet artificial intelligence systems often rely on monolithic architectures that limit adaptabili…
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo +3
Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managin…
Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance
Antoine Grosnit, Alexandre Maraval, Refinath S N +16
Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb's experiential learni…
Zero-shot Model-based Reinforcement Learning using Large Language Models
Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat +6
The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcemen…
Large Language Models as Markov Chains
Oussama Zekri, Ambroise Odonnat, Abdelhakim Benechehab +3
Large language models (LLMs) are remarkably efficient across a wide range of natural language processing tasks and well beyond them. However, a comprehensive theoretical analysis o…
Can LLMs predict the convergence of Stochastic Gradient Descent?
Oussama Zekri, Abdelhakim Benechehab, Ievgen Redko
Large-language models are notoriously famous for their impressive performance across a wide range of tasks. One surprising example of such impressive performance is a recently iden…
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo +2
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data. A critical challenge of this approach i…
Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning
Abdelhakim Benechehab, Albert Thomas, Balázs Kégl
We consider the problem of offline reinforcement learning where only a set of system transitions is made available for policy optimization. Following recent advances in the field,…
Multi-timestep models for Model-based Reinforcement Learning
Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas +2
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data. A critical challenge of this approach i…