4 citations · 8 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…