Jing-Da Wu

Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure

Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor. Their output carries two errors: parallelization error due to parallel sampling, and estimation error from the mask predictor learned with finite samples. Existing theory treats the...

Transforming driving-scenario priors into adaptive reward modulation for reinforcement learning-based energy management of heavy-duty fuel cell vehicles