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Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Given an assignment $\mathbf{x}$ to all variables in $\mathbf{X}$ (evidence) and a real number $q$, the constrained most-probable explanation (CMPE) task seeks to find an assignment $\mathbf{y}$ to all variables in $\mathbf{Y}$ such that $f(\mathbf{x}, \mathbf{y})$ is maximized and $g(\mathbf{x}, \mathbf{y})\leq q$.
Neural Network Approximators for Marginal MAP in Probabilistic Circuits
We evaluate our new approach on several benchmark datasets and show that it outperforms three competing linear time approximations, max-product inference, max-marginal inference and sequential estimation, which are used in practice to solve MMAP tasks in PCs.
Novel Upper Bounds for the Constrained Most Probable Explanation Task
We propose several schemes for upper bounding the optimal value of the constrained most probable explanation (CMPE) problem.
A Novel Approach for Constrained Optimization in Graphical Models
Given two (possibly identical) PGMs $M_1$ and $M_2$ defined over the same set of variables and a real number $q$, find an assignment of values to all variables such that the probability of the assignment is maximized w. r. t.
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
The explanations generated by these simplified models, however, might not accurately justify and be truthful to the model.
