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.
A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks
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abstract
This paper extends the work in [Suzuki, 1996] and presents an efficient depth-first branch-and-bound algorithm for learning Bayesian network structures, based on the minimum description length (MDL) principle, for a given (consistent) variable ordering. The algorithm exhaustively searches through all network structures and guarantees to find the network with the best MDL score. Preliminary experiments show that the algorithm is efficient, and that the time complexity grows slowly with the sample size. The algorithm is useful for empirically studying both the performance of suboptimal heuristic search algorithms and the adequacy of the MDL principle in learning Bayesian networks.
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cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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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.