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, alongside 17 external citations. Polarity classification is still indexing.
4
Pith papers citing it
17
external citations · external index
years
2026 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.
A causal density function is a Radon–Nikodym ratio between interventional and observational laws; the paper's evaluation shows calibration works only where overlap is good and graph scoring largely fails, with internally conflicting result tables.
citing papers explorer
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
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.
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Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
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.
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Causal Density Functions
A causal density function is a Radon–Nikodym ratio between interventional and observational laws; the paper's evaluation shows calibration works only where overlap is good and graph scoring largely fails, with internally conflicting result tables.