3 papers
cs.LG2026
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
cs.LG2026
Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss
Thomas T. Zhang, Alok Shah, Yifei Zhang +3
Many modern applications of deep learning involve training a neural network via a one-step prediction loss (e.g., regression, cross-entropy), but deploy the network by rollin…
cs.LG2025
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
Xinyue Ai, Yutong He, Albert Gu +4
Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet par…