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: We study the classic online learning problem of predicting with expert advice, and propose a truly parameter-free and adaptive algorithm that achieves several objectives simultaneously without using any prior information. The main component of this work is an improved version of the NormalHedge.DT algorithm [Luo and Schapire, 2014], called AdaNormalHedge. On one hand, this new algorithm ensures small regret when the competitor has small loss and almost constant regret when the losses are stochastic. On the other hand, the algorithm is able to compete with any convex combination of the experts simultaneously, with a regret in terms of the relative entropy of the prior and the competitor. This resolves an open problem proposed by Chaudhuri et al. [2009] and Chernov and Vovk [2010]. Moreover, we extend the results to the sleeping expert setting and provide two applications to illustrate the power of AdaNormalHedge: 1) competing with time-varying unknown competitors and 2) predicting almost as well as the best pruning tree. Our results on these applications significantly improve previous work from different aspects, and a special case of the first application resolves another open problem proposed by Warmuth and Koolen [2014] on whether one can simultaneously achieve optimal shifting regret for both adversarial and stochastic losses.
Example Output: Achieving All with No Parameters: Adaptive NormalHedge

Example Input: Automatically generated political event data is an important part of the social science data ecosystem. The approaches for generating this data, though, have remained largely the same for two decades. During this time, the field of computational linguistics has progressed tremendously. This paper presents an overview of political event data, including methods and ontologies, and a set of experiments to determine the applicability of deep neural networks to the extraction of political events from news text.
Example Output: Generating Politically-Relevant Event Data

Example Input: This paper studies the applicability of evolutionary algorithms, particularly, the evolution strategies family to estimation of a degradation parameter (referred as kappa parameter) for the shear design of reinforced concrete beams, a problem which have an expensive computational cost and highly relevant in the design of pillars and reinforced concrete structures, which however, has not been covered extensively in the present literature.
Example Output:
“ANÁLISIS E IMPLEMENTACIÓN DE ALGORITMOS EVOLUTIVOS PARA LA OPTIMIZACIÓN DE SIMULACIONES EN INGENIERÍA CIVIL.”