CURATE: Scaling-Up Differentially Private Causal Graph Discovery

Causal graph discovery (CGD) is the process of estimating the underlying probabilistic graphical model that represents the joint distribution of features of a dataset. CGD algorithms are broadly classified into two categories: (i) constraint-based algorithms, where the outcome depends on conditional...

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Veröffentlicht in:	Entropy (Basel, Switzerland) Jg. 26; H. 11; S. 946
Hauptverfasser:	Bhattacharjee, Payel, Tandon, Ravi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 05.11.2024 MDPI
Schlagworte:	Adaptive algorithms adaptive privacy budgeting Algorithms Analysis Budgeting Budgets causal graph discovery Constraints Datasets differential privacy Estimation Leakage Noise prediction Optimization Optimization techniques Privacy Probability distribution Random variables Statistical analysis New York United States Germany adaptive privacy budgeting causal graph discovery differential privacy
ISSN:	1099-4300, 1099-4300
Online-Zugang:	Volltext
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