A covariance-aware extension of DDIM sampling for pixel-space diffusion models that uses Tweedie's formula and Fourier decomposition to model reverse-process covariance and improves sample quality at low NFE.
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2026 2representative citing papers
DARLING augments RL with change detection to match minimax lower bounds on dynamic regret for piecewise stationary tabular and linear MDPs under separability and reachability conditions.
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Covariance-aware sampling for Diffusion Models
A covariance-aware extension of DDIM sampling for pixel-space diffusion models that uses Tweedie's formula and Fourier decomposition to model reverse-process covariance and improves sample quality at low NFE.
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DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees
DARLING augments RL with change detection to match minimax lower bounds on dynamic regret for piecewise stationary tabular and linear MDPs under separability and reachability conditions.