Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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SBTG recovers the Jacobian of the nonlinear transition map between brain states by multiplying cross-block scores from denoising models, enabling inference of lag-specific directed interactions in neural population data such as C. elegans calcium imaging.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
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Inferring Active Neural Circuits Using Diffusion Scores
SBTG recovers the Jacobian of the nonlinear transition map between brain states by multiplying cross-block scores from denoising models, enabling inference of lag-specific directed interactions in neural population data such as C. elegans calcium imaging.