Aggregating algorithm for prediction of packs

This paper formulates a protocol for prediction of packs, which is a special case of on-line prediction under delayed feedback. Under the prediction of packs protocol, the learner must make a few predictions without seeing the respective outcomes and then the outcomes are revealed in one go. The paper develops the theory of prediction with expert advice for packs by generalising the concept of mixability. We propose a number of merging algorithms for prediction of packs with tight worst case loss upper bounds similar to those for Vovk’s Aggregating Algorithm. Unlike existing algorithms for delayed feedback settings, our algorithms do not depend on the order of outcomes in a pack. Empirical experiments on sports and house price datasets are carried out to study the performance of the new algorithms and compare them against an existing method

Measurements of Inequalities in Access to Higher Education: Case of the Russian Federation

This paper focuses on the systematic investigation of the influence of the quasi-experimental projects on possible changes in inequality in access to higher education in Russia. While intuition points toward a positive effect of introduction of the standardized tests and educational vouchers on the decrease in inequality in access to higher education and more effective, efficient, and fairer distribution of the state funds in higher education, empirical evidence on this issue has yet to be found. This paper presents evaluation techniques for the experiments including introduction of the distribution index (DI) to measure income and education inequalities. The DI, the weighted DI, and the share weighted DI allow capturing changes in inequalities in income distribution, concentration of wealth, or access to higher education more precisely than does the Gini coefficient