Jeffrey guidance applies Jeffrey's rule of conditioning to diffusion models to target prescribed marginal distributions while preserving conditional structure, demonstrated via embedding matching and fairness enforcement.
arXiv preprint arXiv:2403.01633 , year=
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The paper introduces a phase framework for data distributions connected by local denoisers and demonstrates that reverse diffusion consists of trivial and data phases separated by a transition where local score functions must fail, tied to spatial Markovianity.
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
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Towards More General Control of Diffusion Models Using Jeffrey Guidance
Jeffrey guidance applies Jeffrey's rule of conditioning to diffusion models to target prescribed marginal distributions while preserving conditional structure, demonstrated via embedding matching and fairness enforcement.
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Local Diffusion Models and Phases of Data Distributions
The paper introduces a phase framework for data distributions connected by local denoisers and demonstrates that reverse diffusion consists of trivial and data phases separated by a transition where local score functions must fail, tied to spatial Markovianity.
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Statistical Properties of Training & Generalization
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.