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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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A multi-step Richardson-Romberg extrapolation method for stochastic approximation

Stochastic Processes and their Applications. 2015. No. 125. P. 4066–4101.
L.Huang, Frikha N.
We obtain an expansion of the implicit weak discretization error for the target of stochastic approximation algorithms introduced and studied in Frikha (2013). This allows us to extend and develop the Richardson–Romberg extrapolation method for Monte Carlo linear estimator (introduced in Talay and Tubaro (1990) and deeply studied in Pag`es (2007)) to the framework of stochastic optimization by means of stochastic approximation algorithm. We notably apply the method to the estimation of the quantile of diffusion processes. Numerical results confirm the theoretical analysis and show a significant reduction in the initial computational cost.
Priority areas: mathematics
Language: English
Keywords: Euler schemeсхема Эйлераэкстраполяция РичардсонаRichardson-Romberg extrapolationstochastic approximation algorithmweak errorстохастический алгоритм аппроксимации
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