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 Input:
In this paper, we address the problem of embedded feature selection for ranking on top of the list problems. We pose this problem as a regularized empirical risk minimization with p-norm push loss function (p = ∞) and sparsity inducing regularizers. We leverage the issues related to this challenging optimization problem by considering an alternating direction method of multipliers algorithm which is built upon proximal operators of the loss function and the regularizer. Our main technical contribution is thus to provide a numerical scheme for computing the infinite push loss function proximal operator. Experimental results on toy, DNA microarray and BCI problems show how our novel algorithm compares favorably to competitors for ranking on top while using fewer variables in the scoring function.

Ex Output:
Sparse Support Vector Infinite Push


Ex 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.

Ex Output:
Model based Bayesian Exploration


Ex Input:
Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and observe that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the observations, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design simple heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment results show that SynTime outperforms state-of-the-art methods on benchmark datasets and tweets data.

Ex Output:

Time Expression Analysis and Recognition Using Syntactic Types and Simple Heuristic Rules