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.

Input: Consider Input: Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classi cal notion of Value of Informationthe expected im provement in future decision quality arising from the tnformation acquired by exploration. Estimating this quantity requires an assessment of the agent's uncer tainty about its current value estimates for states. In this paper we investigate ways to represent and rea son about this uncertainty in algorithms where the sys tem attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q values based on these. These distributions are used to compute a myopic approximation to the value of infor mation for each action and hence to select the action that best balances exploration and exploitation.

Output: Model based Bayesian Exploration

Input: Consider Input: 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.

Output: Recurrent Coevolutionary Feature Embedding Processes for Recommendation

Input: Consider Input: Local consistency techniques such as k-consistency are a key component of specialised solvers for constraint satisfaction problems. In this paper we show that the power of using k-consistency techniques on a constraint satisfaction problem is precisely captured by using a particular inference rule, which we call negative-hyper-resolution, on the standard direct encoding of the problem into Boolean clauses. We also show that current clauselearning SAT-solvers will discover in expected polynomial time any inconsistency that can be deduced from a given set of clauses using negative-hyper-resolvents of a fixed size. We combine these two results to show that, without being explicitly designed to do so, current clause-learning SAT-solvers efficiently simulate k-consistency techniques, for all fixed values of k. We then give some experimental results to show that this feature allows clause-learning SAT-solvers to efficiently solve certain families of constraint problems which are challenging for conventional constraint-programming solvers.
Output: Local Consistency and SAT-Solvers