Sampling ideas

Diffusion step schedules, solvers, guidance and stopping rules that cut function evaluations or improve sample quality.

2 ideas found

Status: Beats tuned baseline Category: Sampling Math: Statistics Clear all

✓✓ Beats tuned baseline 2026

Posterior-Routed Low-Rank Diffusion Experts

Use the noisy diffusion state to estimate posterior probabilities over data clusters, then route the denoising computation to one or a few cluster-specific score experts. Each expert stores a low-rank covariance or low-rank score correction, so after posterior commitment the denoiser operates at the cluster intrinsic dimension rather than ambient dimension. During the early mixing phase, retain a shared full-dimensional score or evaluate a small weighted set of experts.

Useful8/10

Difficulty6/10

Novelty6/10

Sampling Speedup Memory Accuracy

Paper: Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification arXiv:2608.19067

✓✓ Beats tuned baseline 2026

Conditional-Transport Discrete Reverse Diffusion

Replace the standard Gaussian affine reverse step with a conditional transport kernel learned from the forward transition. Given a noisy state x_{k+1}, the model predicts a full conditional distribution for x_k using a monotone conditional CDF or an autoregressive normalizing flow. This represents multimodal and state-dependent reverse transitions that cannot be captured by a single Gaussian mean and variance.

Useful8/10

Difficulty6/10

Novelty5/10

Sampling Accuracy Stability Sample efficiency

Paper: Reverse-Time Diffusion Processes for Discrete Time Linear and Nonlinear Systems with non-Gaussian Noise arXiv:2607.23947