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  • Проектирование инструментария для выбора методов интерпретируемого машинного обучения
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August 18, 2026
HSE Scholar Presents Research on Postcards in Brazil and South Korea
Timur Khusyainov, Deputy Dean of theFaculty of Humanities atHSE University–Nizhny Novgorod, took part in two international conferences—the XVI World Congress of Rural Sociology in Porto Alegre, Brazil, and the 36th Annual Conference of the Alliance of Digital Humanities Organisations (DH2026) in Daejeon, South Korea. On his way to the conferences, the researcher also visited several other places, where he presented the experience of the Pochtovoe educational project.
August 18, 2026
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.
August 17, 2026
‘I Dream of Simple Things
Anastasia Gergenreter specialises in applied statistics and econometrics. In this interview for the Young Scientists of HSE University project, she talked about why she studies addictive substance use, two very different Fishers, and the cherry blossom season at the Main Botanical Garden in Moscow.

 

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Проектирование инструментария для выбора методов интерпретируемого машинного обучения

С. 247–257.
Suvorova A., Смирнова А. В.

Interpretable Machine Learning (IML) methods provide valuable insights into model behaviour, but due to variety of available interpretation tools, the particular solution can be not the optimal fit to the needs of the target users. To support decision making in model building and exploration, we propose a modular framework for algorithm selection. The framework is based on the overview of properties of IML methods, available metrics and existing guidelines for explanation systems development and evaluation.

Language: Russian
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Keywords: метрики качестваQuality metricsexplainable AIобъяснимый ИИAlgorithm Selectionвыбор алгоритма

In book

Интегрированные модели и мягкие вычисления в искусственном интеллекте ИММВ-2022. Сборник научных трудов XI Международной научно-практической конференции. В 2-х томах, том 1
Т. 1. , Универсум, 2022.
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