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.

Q: <lb>Recent price-of-anarchy analyses of games of complete information suggest that coarse correlated<lb>equilibria, which characterize outcomes resulting from no-regret learning dynamics, have near-optimal<lb>welfare. This work provides two main technical results that lift this conclusion to games of incomplete<lb>information, a.k.a., Bayesian games. First, near-optimal welfare in Bayesian games follows directly from<lb>the smoothness-based proof of near-optimal welfare in the same game when the private information<lb>is public. Second, no-regret learning dynamics converge to Bayesian coarse correlated equilibrium in<lb>these incomplete information games. These results are enabled by interpretation of a Bayesian game<lb>as a stochastic game of complete information.

A: No-Regret Learning in Repeated Bayesian Games
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Q: Well-established automatic analyses of texts mainly consider frequencies of linguistic units, e.g. letters, words and bigrams, while methods based on cooccurrence networks consider the structure of texts regardless of the nodes label (i.e. the words semantics). In this paper, we reconcile these distinct viewpoints by introducing a generalized similarity measure to compare texts which accounts for both the network structure of texts and the role of individual words in the networks. We use the similarity measure for authorship attribution of three collections of books, each composed of 8 authors and 10 books per author. High accuracy rates were obtained with typical values from 90% to 98.75%, much higher than with the traditional the TF-IDF approach for the same collections. These accuracies are also higher than taking only the topology of networks into account. We conclude that the different properties of specific words on the macroscopic scale structure of a whole text are as relevant as their frequency of appearance; conversely, considering the identity of nodes brings further knowledge about a piece of text represented as a network.

A: On the role of words in the network structure of texts: application to authorship attribution
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Q: Analogy Based Effort Estimation (ABE) is one of the prominent methods for software effort estimation. The fundamental concept of ABE is closer to the mentality of expert estimation but with an automated procedure in which the final estimate is generated by reusing similar historical projects. The main key issue when using ABE is how to adapt the effort of the retrieved nearest neighbors. The adaptation process is an essential part of ABE to generate more successful accurate estimation based on tuning the selected raw solutions, using some adaptation strategy. In this study we show that there are three interrelated decision variables that have great impact on the success of adaptation method: (1) number of nearest analogies (k), (2) optimum feature set needed for adaptation, and (3) adaptation weights. To find the right decision regarding these variables, one need to study all possible combinations and evaluate them individually to select the one that can improve all prediction evaluation measures. The existing evaluation measures usually behave differently, presenting sometimes opposite trends in evaluating prediction methods. This means that changing one decision variable could improve one evaluation measure while it is decreasing the others. Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus, the overall evaluation measures without degrading the others. The impact of these decisions together has not been investigated before, therefore we propose to view the building of adaptation procedure as a multi-objective optimization problem. The Particle Swarm Optimization Algorithm (PSO) is utilized to find the optimum solutions for such decision variables based on optimizing multiple evaluation measures. We evaluated the proposed approaches over 15 datasets and using 4 evaluation measures. After extensive experimentation we found that: (1) predictive performance of ABE has noticeably been improved, (2) optimizing all decision variables together is more efficient than ignoring any one of them. (3) Optimizing decision variables for each project individually yield better accuracy than optimizing them for the whole dataset.

A:
Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation
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