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]: Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian network structure learning under graph theoretic restrictions on the (directed) super-structure. The super-structure is an undirected graph that contains as subgraphs the skeletons of solution networks. We introduce the directed super-structure as a natural generalization of its undirected counterpart. Our results apply to several variants of score-based Bayesian network structure learning where the score of a network decomposes into local scores of its nodes. Results: We show that exact Bayesian network structure learning can be carried out in non-uniform polynomial time if the super-structure has bounded treewidth, and in linear time if in addition the super-structure has bounded maximum degree. Furthermore, we show that if the directed super-structure is acyclic, then exact Bayesian network structure learning can be carried out in quadratic time. We complement these positive results with a number of hardness results. We show that both restrictions (treewidth and degree) are essential and cannot be dropped without loosing uniform polynomial time tractability (subject to a complexity-theoretic assumption). Similarly, exact Bayesian network structure learning remains NP-hard for "almost acyclic" directed super-structures. Furthermore, we show that the restrictions remain essential if we do not search for a globally optimal network but aim to improve a given network by means of at most k arc additions, arc deletions, or arc reversals (k-neighborhood local search).
[EX A]: Parameterized Complexity Results for Exact Bayesian Network Structure Learning

[EX Q]: Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. To accurately capture the fine grained nonlinear coevolution of these features, we propose a recurrent coevolutionary feature embedding process model, which combines recurrent neural network (RNN) with a multidimensional point process model. The RNN learns a nonlinear representation of user and item features which take into account mutual influence between user and item features, and the feature evolution over time. We also develop an efficient stochastic gradient algorithm for learning the model parameters, which can readily scale up to millions of events. Experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.
[EX A]: Recurrent Coevolutionary Feature Embedding Processes for Recommendation

[EX Q]: We develop a latent variable model and an efficient spectral algorithm motivated<lb>by the recent emergence of very large data sets of chromatin marks from multiple<lb>human cell types. A natural model for chromatin data in one cell type is a Hidden<lb>Markov Model (HMM); we model the relationship between multiple cell types by<lb>connecting their hidden states by a fixed tree of known structure.<lb>The main challenge with learning parameters of such models is that iterative meth-<lb>ods such as EM are very slow, while naive spectral methods result in time and<lb>space complexity exponential in the number of cell types. We exploit properties<lb>of the tree structure of the hidden states to provide spectral algorithms that are<lb>more computationally efficient for current biological datasets. We provide sample<lb>complexity bounds for our algorithm and evaluate it experimentally on biological<lb>data from nine human cell types. Finally, we show that beyond our specific model,<lb>some of our algorithmic ideas can be applied to other graphical models.
[EX A]:
Spectral Learning of Large Structured HMMs for Comparative Epigenomics