**Convolutional Highways**

Convolutional highways are deep networks based on multiple stacked convolutional layers for feature preprocessing. … **Decentralized High-Dimensional Bayesian Optimization (DEC-HBO)**

This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objective function f for boosting the BO performance and still preserve scalability in the number of input dimensions without requiring prior knowledge or the existence of a low (effective) dimension of the input space. To realize this, we propose a sparse yet rich factor graph representation of f to be exploited for designing an acquisition function that can be similarly represented by a sparse factor graph and hence be efficiently optimized in a decentralized manner using distributed message passing. Despite richly characterizing the interdependent effects of the input components on the output of f with a factor graph, DEC-HBO can still guarantee no-regret performance asymptotically. Empirical evaluation on synthetic and real-world experiments (e.g., sparse Gaussian process model with 1811 hyperparameters) shows that DEC-HBO outperforms the state-of-the-art HBO algorithms. … **Computer-Assisted Fraud Detection**

The automatic detection of frauds in banking transactions has been recently studied as a way to help the analysts finding fraudulent operations. Due to the availability of a human feedback, this task has been studied in the framework of active learning: the fraud predictor is allowed to sequentially call on an oracle. This human intervention is used to label new examples and improve the classification accuracy of the latter. Such a setting is not adapted in the case of fraud detection with financial data in European countries. Actually, as a human verification is mandatory to consider a fraud as really detected, it is not necessary to focus on improving the classifier. We introduce the setting of ‘Computer-assisted fraud detection’ where the goal is to minimize the number of non fraudulent operations submitted to an oracle. The existing methods are applied to this task and we show that a simple meta-algorithm provides competitive results in this scenario on benchmark datasets. … **Age Period Cohort Model (APC)**

Age-Period-Cohort models is a class of models for demographic rates (mortality/morbidity/fertility/…) observed for a broad age range over a reasonably long time period, and classified by age and date of follow-up (period) and date of birth (cohort). This type of follow-up can be shown in a Lexis-diagram; a coordinate system with data of follow-up along the x-axis, and age along the y-axis. A single persons life-trajectory is therefore a straight line with slope 1 (as calender time and age advance at the same pace). Tabulated data enumerates the number of events and the risk time (sum of lengths of life-trajectories) in some subsets of the Lexis diagram, usually subsets classified by age and period in equally long intervals. Individual life-lines can be shown with colouring according to states, or the diagram can just be shown to indicate what ages and periods are covered, and what subsets are used for classification of events and risk time. The Age-Period-Cohort model describes the (log)rates as a sum of (non-linear) age- period- and cohort-effects. The three variables age (at follow-up), a, period (i.e. date of follow-up), p, and cohort (date of birth), c, are related by a=p-c – any one person’s age is calculated by subtracting the date of birth from the current date. Hence the three variables used to describe rates are linearly related, and the model can therefore be parametrized in different ways, and still produce the same estimated rates. In popular terms you can say that it is possible to move two linear effects around between the three terms, because the age-terms contains the linear effect of age, the period-terms contains the linear effect of period and the cohort effect contains the linear effect of cohort. An illustration of this phenomenon is in this little “film” of APC-effects on testis cancer rates in Denmark. All sets of estimates will yield the same set of fitted rates. …

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