In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: This paper is about reducing influence dia­ gram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to read­ ily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduc­ tion methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN in­ ference problems induced by the mew method are much easier to solve than those induced by the two previous methods.
[EX A]: Probabilistic Inference in Influence Diagrams

[EX Q]: Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computing the anomalous degree of a network sample is generally not sufficient. In fact, explaining why the network is exceptional, expressed in the form of subnetwork, is also equally important. In this paper, we develop a novel algorithm to address these two key problems. We treat each network sample as a potential outlier and identify subnetworks that mostly discriminate it from nearby regular samples. The algorithm is developed in the framework of network regression combined with the constraints on both network topology and L1-norm shrinkage to perform subnetwork discovery. Our method thus goes beyond subspace /subgraph discovery and we show that it converges to a global optimum. Evaluation on various real-world network datasets demonstrates that our algorithm not only outperforms baselines in both network and high dimensional setting, but also discovers highly relevant and interpretable local subnetworks, further enhancing our understanding of anomalous networks.
[EX A]: Outlier Detection from Network Data with Subnetwork Interpretation

[EX Q]: In this correspondence, we will point out a problem with testing adaptive classifiers on autocorrelated data. In such a case random change alarms may boost the accuracy figures. Hence, we cannot be sure if the adaptation is working well.
[EX A]:
How good is the Electricity benchmark for evaluating concept drift adaptation