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.

[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.
[A]: Outlier Detection from Network Data with Subnetwork Interpretation

[Q]: This paper concerns the probabilistic evalu ation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a sin gleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.
[A]: Testing Identifiability of Causal Effects

[Q]: <lb>Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the<lb>multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide<lb>whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been<lb>simply ignored by the user. The present work proposes to exploit available information regarding the display<lb>position bias under the so-called Position-based click model (PBM). We first discuss how this model differs<lb>from the Cascade model and its variants considered in several recent works on multiple-play bandits. We<lb>then provide a novel regret lower bound for this model as well as computationally efficient algorithms that<lb>display good empirical and theoretical performance.
[A]:
Multiple-Play Bandits in the Position-Based Model