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 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.
[EX A]: No-Regret Learning in Repeated Bayesian Games

[EX Q]: Existing studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and then adapt it to the target domain. Due to the diversity of logical forms in different domains, this problem presents unique and intriguing challenges. By converting logical forms into canonical utterances in natural language, we reduce semantic parsing to paraphrasing, and develop an attentive sequence-to-sequence paraphrase model that is general and flexible to adapt to different domains. We discover two problems, small micro variance and large macro variance, of pretrained word embeddings that hinder their direct use in neural networks, and propose standardization techniques as a remedy. On the popular OVERNIGHT dataset, which contains eight domains, we show that both cross-domain training and standardized pre-trained word embedding can bring significant improvement.
[EX A]: Cross-domain Semantic Parsing via Paraphrasing

[EX Q]: Inference in general Ising models is difficult, due to high treewidth making treebased algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.
[EX A]:
Projecting Ising Model Parameters for Fast Mixing