4 citations · 8 across the 11 of their papers we have counts for
11 papers
Solving a Million-Step LLM Task with Zero Errors
Elliot Meyerson, Giuseppe Paolo, Roberto Dailey +6
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by h…
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…
Post-Training Corrections for Improved Time-Series Forecasting
Hamza Cherkaoui, Malik Tiomoko, Giuseppe Paolo +4
Time-series forecasting is a critical task in various business domains, but it remains inherently challenging. Typically, large forecasting models are trained in a single, resource…
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…
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…
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov +4
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multiv…
A call for embodied AI
Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language M…
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…