A single diffusion policy network with per-factor null-token dropout enables additive score composition for robot control under conditional independence, with a trajectory-tube certificate, shown to generalize on drone racing tasks.
Transactions on Machine Learning Research (TMLR) , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
citing papers explorer
-
Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network
A single diffusion policy network with per-factor null-token dropout enables additive score composition for robot control under conditional independence, with a trajectory-tube certificate, shown to generalize on drone racing tasks.
-
Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.