Occurrence-level attribution in finite-horizon adaptive learning is defined via a conditional interventional target, shown to be unrecoverable from replay data in general but identifiable in a specific structural class from logged data.
Erdogdu, Richard E
2 Pith papers cite this work. Polarity classification is still indexing.
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MUCS uses mirrored unlearning and noise-consistent skew to outperform prior TDA methods for diffusion models on three datasets.
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Data Attribution in Adaptive Learning
Occurrence-level attribution in finite-horizon adaptive learning is defined via a conditional interventional target, shown to be unrecoverable from replay data in general but identifiable in a specific structural class from logged data.
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Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew
MUCS uses mirrored unlearning and noise-consistent skew to outperform prior TDA methods for diffusion models on three datasets.