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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
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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.

 

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Minimal envy and popular matchings

European Journal of Operational Research. 2022. Vol. 296. No. 3. P. 776–787.
Kondratev A., Nesterov A. S.

We study ex-post fairness and efficiency in the object allocation problem. A matching is individually
fair if it minimizes the number of envying agents, we call it minimal envy matching, and conditional
on being minimal envy also minimizes the number of envying agents in a reduced problem, we call it
minimal envy-2 matching. A matching is socially fair if supported by the majority of agents against any
other matching – popular matching. We observe that when a popular matching exists it is equivalent to a
minimal envy-2 matching.
We show the equivalence between global and local popularity: a matching is popular if and only if
in no group of size up to 3 agents a majority can benefit by exchanging objects, keeping the remaining matching fixed. We algorithmically show that an arbitrary matching is path-connected with a popular matching by locally-popular exchanges in small groups. Thus a corresponding market converges to a
popular matching.
Popular matchings often do not exist. We define most popular matching as a matching that is popular
among the largest (by cardinality) subset of agents. We show that a matching is minimal envy-2 if and
only if it is minimal envy and most popular, and propose a polynomial-time algorithm to find a Pareto
efficient minimal envy-2 matching.

Research target: Economics and Management Mathematics
Priority areas: economics mathematics
Language: English
Full text
DOI
Text on another site
Keywords: Assignmentpopular matchingsobject allocationex-post fairnessminimal envyпопулярные мэтчингиминимальная завистьэкс-пост справедливостьзадача о сочетаниях
Publication based on the results of:
Economic mechanisms: information design and robustness to manipulation (2021)
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