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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
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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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Random Walks in Nonhomogeneous Poisson Environment

P. 3–24.
Davydov Y., Konakov V.

In the first part of the paper we consider a "random flight" process in  \(R^d\)   and obtain the weak limits under different transformations of the Poissonian switching times.  In the second part we construct diffusion approximations for this process and investigate their accuracy. To prove the weak convergence result we use the approach of Stroock and Varadhan (1979).  We consider more general model which may be called  "random walk over ellipsoids in  \(R^d\)".  For this model we establish the Edgeworth type expansion. The main tool in this part is the parametrix method (Konakov (2012), Konakov and Mammen (2009)).

Language: English
Full text
Keywords: случайные блужданияметод параметриксаrandom walksparametrix methodrandom flightsRandom nonhomogeneous environmentDiffusion approximationслучайная неоднородная среда
Publication based on the results of:
Вероятностные и статистические методы анализа сложных моделей, задаваемых стохастическими дифференциальными и разностными уравнениями (2017)

In book

Modern problems of stochastic analysis and statistics - Selected contributions in honor of Valentin Konakov
Modern problems of stochastic analysis and statistics - Selected contributions in honor of Valentin Konakov
Heidelberg: Springer, 2017.
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