Jasmina Bogojeska

TL;DR: This work derives a solution that produces resampling weights which match the pool of all examples to the target distribution of any given task, and substantially improves the overall prediction accuracy.

TL;DR: A highly accurate SMART-based analysis pipeline that can correctly predict the necessity of a disk replacement even 10-15 days in advance and uses statistical techniques to automatically detect which SMART parameters correlate with disk replacement.

TL;DR: Evaluating viral and host characteristics associated with reservoir size and long-term dynamics in 1,057 individuals on suppressive antiretroviral therapy shows that in 26.6% of individuals the reservoir increases, and viral blips and low-level viremia are significantly associated with slower reservoir decay.

TL;DR: A mixture-of-experts model is proposed that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual and it is verified that therapy combinations proposed using this approach significantly outperform previous methods.

TL;DR: This paper identifies and rank servers with problematic behavior as candidates for modernization using a random forest classifier and chooses a predictive model that yields high quality predictions and outperforms traditional linear regression models on a large set of real data.