8 papers
Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning
Minhao Yao, Ruoyu Wang, Xihong Lin +2
Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early stopping ru…
Exploring the Design Space of Reward Backpropagation for Flow Matching
Ruoyu Wang, Boye Niu, Xiangxin Zhou +3
Aligning text-to-image flow matching models with human preferences via direct reward backpropagation is sample-efficient but hampered by two well-known pathologies: activations can…
Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics
Ruoyu Wang, Xihong Lin
Knowledge transfer across data sources holds great promise for improving the estimation of target population parameters by leveraging the growing availability of data from differen…
Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance
Liangyu Yuan, Yufei Huang, Mingkun Lei +5
Diffusion models generate synthetic images through an iterative refinement process. However, the misalignment between the simulation-free objective and the iterative process often…
Few-Step Diffusion Sampling Through Instance-Aware Discretizations
Liangyu Yuan, Ruoyu Wang, Tong Zhao +4
Diffusion and flow matching models generate high-fidelity data by simulating paths defined by Ordinary or Stochastic Differential Equations (ODEs/SDEs), starting from a tractable p…
Parallel Diffusion Solver via Residual Dirichlet Policy Optimization
Ruoyu Wang, Ziyu Li, Beier Zhu +5
Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based a…