The aggregated search assembles in one interface information from different sources. It deals with different types of content (text, video, image, etc) and granularities of retrieval. It aims to assemble the retrieved content forming an aggregated result. This is in contrast with the common approach which provides a list of documents as the search result. Today we are able to retrieve content o...

Abstract Large‐scale Gaussian process (GP) regression is infeasible for large training data due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies recent literature focus on divide‐and‐conquer, example, partitioning into subproblems inducing functional (and thus computational) independence. Such approximations can be speedy, accurate, sometime...

In this paper, we study the aggregated risk from dependent risk factors under the multivariate Extreme Value Theory (EVT) framework. We consider the heavy-tailness of the risk factors as well a non-parametric tail dependence structure. This allows a large scope of models on the dependency. We assess the Value-at-Risk of a diversified portfolio constructed from dependent risk factors. Moreover, ...

In this article, we aim to improve the prediction from experts’ aggregation by using underlying properties of models that provide experts involved in procedure. We restrict ourselves case where perform their predictions fitting state-space data Kalman recursions. Using exponential weights, construct different recursions Aggregated Online (KAO) algorithms compete with best expert or convex combi...

This study on explaining aggregated recovery rates (ARR) is based the largest existing loss and database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents over 120 countries. The dependence of monthly ARR bank various macroeconomic factors examined sources their variability are stated. For first time, an influence stochastically estimated growth GDP ...