Tag: samplers
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Corrected Samplers for Discrete Flow Models
Corrected Samplers for Discrete Flow Models arXiv:2601.22519v1 Announce Type: new Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on a finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete diffusion models, such as tau-leaping and…
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One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow
One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow arXiv:2512.05251v1 Announce Type: new Abstract: Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterative steps to produce high-quality samples, leading to high computational costs. We introduce one-step diffusion samplers which learn a step-conditioned…
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Improving the evaluation of samplers on multi-modal targets
Improving the evaluation of samplers on multi-modal targets arXiv:2504.08916v1 Announce Type: new Abstract: Addressing multi-modality constitutes one of the major challenges of sampling. In this reflection paper, we advocate for a more systematic evaluation of samplers towards two sources of difficulty that are mode separation and dimension. For this, we propose a synthetic experimental setting…
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Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization arXiv:2503.15704v1 Announce Type: new Abstract: The performance of sequential Monte Carlo (SMC) samplers heavily depends on the tuning of the Markov kernels used in the path proposal. For SMC samplers with unadjusted Markov kernels, standard tuning objectives, such as the Metropolis-Hastings acceptance rate or the…