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  • Алгоритмическое решающее правило, использующее ординальные коэффициенты важности критериев со шкалой первой порядковой метрики
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News
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.

 

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Алгоритмическое решающее правило, использующее ординальные коэффициенты важности критериев со шкалой первой порядковой метрики

Журнал вычислительной математики и математической физики. 2012. Т. 52. № 1. С. 48–65.
Nelyubin A. P., Podinovskiy V. V.

An exact computational method is proposed for the preferability comparison of various solution variants in multicriteria problems withimportance[1]ordered criteria using a common scale along which the growth of preferences slows down.

Priority areas: business informatics mathematics
Language: Russian
Full text
Keywords: criteria importance theoryтеория важности критериевкоэффициенты важности критериевmulticriteria decision making problemsimportance coefficientsfirst ordinal metric scaleмногокритериальные задачи принятия решенийшкала первой порядковой метрики
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