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.

Example Input: Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the "right" latent task structure should be learned in a data-driven manner. We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. The nonparametric aspect makes the model expressive enough to subsume many existing models of latent task structures (e.g, meanregularized tasks, clustered tasks, low-rank or linear/non-linear subspace assumption on tasks, etc.). Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. Experimental results on synthetic and realworld datasets, on both regression and classification problems, demonstrate the effectiveness of the proposed method.
Example Output: Flexible Modeling of Latent Task Structures in Multitask Learning

Example Input: Encoding finite linear CSPs as Boolean formulas and solving them by using modern SAT solvers has proven to be highly effective, as exemplified by the award-winning sugar system. We here develop an alternative approach based on ASP. This allows us to use first-order encodings providing us with a high degree of flexibility for easy experimentation with different implementations. The resulting system aspartame re-uses parts of sugar for parsing and normalizing CSPs. The obtained set of facts is then combined with an ASP encoding that can be grounded and solved by off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting aspartame and sugar.
Example Output: Aspartame: Solving Constraint Satisfaction Problems with Answer Set Programming

Example Input: In previous work [BGHK92, BGHK93], we have studied the random-worlds approacha particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowl edge base consisting of objective (first-order, slalisti cal, and defaull) infonnation. But allowing a know l edge base to contain only objective information is sometimes limiting. We occa<;ionally wish to include infonnation about degrees of belief in the knowledge base as well, because there are contexts in which old be1iefs represent important information that should influence new beliefs. In this paper, we describe three quite general techniques for extending a method that generates degrees of belief from objective informa tion to one that can make use of degrees of belief as well. All of our techniques are ha<;ed on well-known approaches, such a5 cross-emropy. We discuss gen eral connections between the techniques and in partic ular show that, although conceptually and technically quite different, all of the techniques give the same answer when applied to the random-worlds method.
Example Output:
Generating New Beliefs From Old*