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]: 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.
[A]: Top-N Recommendation on Graphs


[Q]: Metric learning seeks a transformation of the feature space that enhances prediction quality for the given task at hand. In this work we provide PAC-style sample complexity rates for supervised metric learning. We give matching lowerand upper-bounds showing that the sample complexity scales with the representation dimension when no assumptions are made about the underlying data distribution. However, by leveraging the structure of the data distribution, we show that one can achieve rates that are fine-tuned to a specific notion of intrinsic complexity for a given dataset. Our analysis reveals that augmenting the metric learning optimization criterion with a simple norm-based regularization can help adapt to a dataset’s intrinsic complexity, yielding better generalization. Experiments on benchmark datasets validate our analysis and show that regularizing the metric can help discern the signal even when the data contains high amounts of noise.
[A]: Sample Complexity of Learning Mahalanobis Distance Metrics


[Q]: We introduce Deep Neural Programs (DNP), a novel programming paradigm for writing adaptive controllers for cyber-physical systems (CPS). DNP replace if and while statements, whose discontinuity is responsible for undecidability in CPS analysis, intractability in CPS design, and frailness in CPS implementation, with their smooth, neural nif and nwhile counterparts. This not only makes CPS analysis decidable and CPS design tractable, but also allows to write robust and adaptive CPS code. In DNP the connection between the sigmoidal guards of the nif and nwhile statements has to be given as a Gaussian Bayesian network, which reflects the partial knowledge, the CPS program has about its environment. To the best of our knowledge, DNP are the first approach linking neural networks to programs, in a way that makes explicit the meaning of the network. In order to prove and validate the usefulness of DNP, we use them to write and learn an adaptive CPS controller for the parallel parking of the Pioneer rovers available in our CPS lab.
[A]:
Deep Neural Programs for Adaptive Control in Cyber-Physical Systems