FHE-based causal structure learning with circuit simplification, Newton-Raphson and Taylor approximations for division/log, and SIMD batching produces structures comparable to plaintext versions.
Learning Bayesian Networks from Incomplete Data with Stochastic Search Algorithms
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the expectation-maximization algorithm. These approaches are guaranteed to find local maxima, but do not explore the landscape for other modes. Our approach evolves structure and the missing data. We compare our stochastic algorithms and show they all produce accurate results.
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption
FHE-based causal structure learning with circuit simplification, Newton-Raphson and Taylor approximations for division/log, and SIMD batching produces structures comparable to plaintext versions.