2016/10/08 by Yexiang Xue, Zhiyuan Li, Xue, Yexiang +7
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Multi-Criteria Decision Making #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1610.02591
openalex publication_date 2016/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them. We propose XORMMAP, a novel approach to solve the Marginal MAP Problem, which represents the intractable counting subproblem with queries to NP oracles, subject to additional parity constraints. XORMMAP provides a constant factor approximation to the Marginal MAP Problem, by encoding it as a single optimization in polynomial size of the original problem. We evaluate our approach in several machine learning and decision making applications, and show that our approach outperforms several state-of-the-art Marginal MAP solvers.