3 papers
stat.ML2026
Tail Annealing for Heavy-Tailed Flow Matching
Jean Pachebat
Standard generative models struggle with heavy-tailed data: Lipschitz architectures cannot produce power-law tails from Gaussian noise, and interpolating between heavy-tailed data…
stat.ML2025
Iterative Tilting for Diffusion Fine-Tuning
Jean Pachebat, Giovanni Conforti, Alain Durmus +1
We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt int…
cs.LG2022
High-Order Optimization of Gradient Boosted Decision Trees
Jean Pachebat, Sergei Ivanov
Gradient Boosted Decision Trees (GBDTs) are dominant machine learning algorithms for modeling discrete or tabular data. Unlike neural networks with millions of trainable parameters…