Self Exciting Point Process (SEPP) google
A point process N is called self-exciting if cov(N(s,t),N(t,u))>0 for s<t<u where here, cov denotes the covariance of the two quantities. Intuitively, a process is self-exciting if the occurrence of past points makes the occurrence of future points more probable.
Spatio-Temporal Modeling with R: Point process prediction for mortals


Context- Aware Bandits (CAB) google
In this paper, we present the CAB (Context- Aware Bandits). With CAB we attempt to craft a bandit algorithm that can exploit collaborative effects and that can be deployed in a practical recommendation system setting, where the multi-armed bandits have been shown to perform well in particular with respect to the cold start problem. CAB exploits, a context-aware clustering technique augmenting exploration-exploitation strategies in a contextual multi-armed bandit settings. CAB dynamically clusters the users based on the content universe under consideration. We demonstrate the efficacy of our approach on extensive real-world datasets, showing the scalability, and more importantly, the significant increased prediction performance compared to related state-of-the-art methods. …

SuperPivot google
We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting – to the best of our knowledge – the largest crosslingual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting assumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation. …

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