ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Differentiable causal discovery from interventional data, 2020 a
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
Causal density functions are Radon-Nikodym derivatives serving as local density ratios between do and obs distributions, allowing observational expectations reweighted by the ratio to reproduce interventional ones.
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