2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…