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Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models
Hamza Cherkaoui, Hélène Halconruy, Antonio Ocello
Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches often use heavy-tailed noise or…
Do Heavy Tails Help Diffusion? On the Subtle Trade-off Between Initialization and Training
Hamza Cherkaoui, Hélène Halconruy, Antonio Ocello
Recent works have proposed incorporating heavy-tailed (HT) noise into diffusion- and flow-based generative models, with the goals of better recovering the tails of target distribut…
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…
Adaptive Sample Sharing for Multi Agent Linear Bandits
Hamza Cherkaoui, Merwan Barlier, Igor Colin
The multi-agent linear bandit setting is a well-known setting for which designing efficient collaboration between agents remains challenging. This paper studies the impact of data…