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.

We consider linear models for stochastic dynamics. To any such model can be as-<lb>sociated a network (namely a directed graph) describing which degrees of freedom<lb>interact under the dynamics. We tackle the problem of learning such a network<lb>from observation of the system trajectory over a time interval T .<lb>We analyze the l1-regularized least squares algorithm and, in the setting in which<lb>the underlying network is sparse, we prove performance guarantees that are uni-<lb>form in the sampling rate as long as this is sufficiently high. This result substan-<lb>tiates the notion of a well defined ‘time complexity’ for the network inference<lb>problem. keywords: Gaussian processes, model selection and structure learning, graphical models, sparsity<lb>and feature selection.
Learning Networks of Stochastic Differential Equations

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.
Top-N Recommendation on Graphs

<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.

Multiple-Play Bandits in the Position-Based Model