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.
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Question: 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.

Answer: Learning Networks of Stochastic Differential Equations


Question: 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.

Answer: Top-N Recommendation on Graphs


Question: We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests. In contrast to prior work, we focus on the challenging, yet practically relevant setting where test outcomes can be conditionally dependent given the hidden target variable. Under such assumptions, common heuristics, such as greedily performing tests that maximize the reduction in uncertainty of the target, often perform poorly. In this paper, we propose ECED, a novel, computationally efficient active learning algorithm, and prove strong theoretical guarantees that hold with correlated, noisy tests. Rather than directly optimizing the prediction error, at each step, ECED picks the test that maximizes the gain in a surrogate objective, which takes into account the dependencies between tests. Our analysis relies on an information-theoretic auxiliary function to track the progress of ECED, and utilizes adaptive submodularity to attain the near-optimal bound. We demonstrate strong empirical performance of ECED on two problem instances, including a Bayesian experimental design task intended to distinguish among economic theories of how people make risky decisions, and an active preference learning task via pairwise comparisons.

Answer:
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests