Константин Вячеславович Воронцов

Отбор признаков, оценивание и выбор моделей. Лекция курса «Машинное обучение» К. В. Воронцова. Цели и подходы отбора признаков. Фильтры, обёртки и встроенные методы.

Обобщения линейных моделей. Лекция курса «Машинное обучение» К. В. Воронцова. Метод Ньютона-Рафсона, метод Ньютона-Гаусса.

Линейные модели регрессии. Лекция курса «Машинное обучение» К. В. Воронцова. Задача регрессии, многомерная линейная регрессия.

Метод опорных векторов. Лекция курса «Машинное обучение» К. В. Воронцова. Оптимальная разделяющая гиперплоскость. Понятие зазора между классами (margin).

Метрические методы классификации и регрессии. Лекция курса «Машинное обучение» К. В. Воронцова. Гипотезы компактности и непрерывности.

Нейронные сети: градиентные методы оптимизации. Лекция курса «Машинное обучение» К. В. Воронцова. Биологический нейрон, модель МакКаллока-Питтса как линейный классификатор. Функции активации.

Стохастический градиент и линейный классификатор. Лекция курса «Машинное обучение» К. В. Воронцова. Метод стохастического градиента SG.

Основные понятия и примеры прикладных задач. Лекция курса «Машинное обучение» К. В. Воронцова. Постановка задач обучения по прецедентам. Объекты и признаки. Типы шкал: бинарные, номинальные, порядковые, количественные.

Modern AI systems often comprise multiple learnable components that can be naturally organized as graphs. A central challenge is the end-to-end training of such systems without restrictive architectural or training assumptions. Such tasks fit the theory and approaches of the collaborative Multi-Agent Reinforcement Learning (MARL) field. We introduce Reinforcement Networks, a general framework for MARL that organizes agents as vertices in a directed acyclic graph (DAG). This structure extends hierarchical RL to arbitrary DAGs, enabling flexible credit assignment and scalable coordination while avoiding strict topologies, fully centralized training, and other limitations of current approaches. We formalize training and inference methods for the Reinforcement Networks framework and connect it to the LevelEnv concept to support reproducible construction, training, and evaluation. We demonstrate the effectiveness of our approach on several collaborative MARL setups by developing several Reinforcement Networks models that achieve improved performance over standard MARL baselines. Beyond empirical gains, Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems. We conclude with theoretical and practical directions - richer graph morphologies, compositional curricula, and graph-aware exploration. That positions Reinforcement Networks as a foundation for a new line of research in scalable, structured MARL.

Text trees as a data structure occur in numerous machine learning tasks like hierarchical summarization and automatic mind map generation. One of the main methods of quality evaluation in these tasks is comparison with reference hierarchies created by experts. The method used so far to compare text hierarchies, as shown in this work, poorly accounts for their structure and text semantics relative to phrasing. To address this issue, we propose a new metric on the set of text trees — text tree edit distance (TTED), based on tree edit distance with semantic distance between texts measured using a large language model. To evaluate how the metric reflects different aspects of text tree difference, we introduce special quality coefficients that reflect the sensitivity of a metric to paraphrasing relative to structural and semantic differences of text trees. Using these coefficients, we conduct extensive testing of the proposed metric and its modifications compared to a baseline used in previous works to compare text hierarchies, which shows that TTED indeed captures significant differences between text trees more accurately than the previously used method. We also provide a practical implementation of TTED for further usage.

Against the backdrop of active LLM development, their tendency to hallucinate, as well as the growing volume of texts they generate, the validation of facts has become increasingly important and relevant.We propose ruSciFact 1 , a new benchmark for fact-checking scientific claims in Russian.ruSciFact is structured as a NLI task, the goal is to verify whether a fact is confirmed by a given abstract.To generate facts, we used an 3-step pipeline based on LLaMA-405B, validating the resulting sentences with the help of assessors-terminologists.The ruSciFact dataset consists of 1128 pairs in the format , which we are releasing as open source together with the benchmark code.Additionally, we are opensourcing the fact-generation pipeline 2 , which facilitates the expansion of the dataset to specific scientific domains.We evaluated several popular language models on ruSciFact, including text embedders and generative models.The results show that this benchmark allows to effectively assess the fact-checking capabilities of LLMs in Russian.

A wide class of natural language processing tasks is solved using markup.At the moment, the vast majority of models and datasets rely on a simple markup structure containing only fragments and labels.Moreover, simple classification metrics such as F1, Precision, Recall are used to evaluate the model's accuracy.The problem with such metrics is that they do not take into account all aspects of the markup structure and that they are applicable only under the assumption of the existence of an ideal markup.This paper proposes a more general and universal markup structure that allows solving complex problems and builds a methodology for multi-criteria evaluation of text markup models based on inconsistent expert markup.After that, the application of the constructed method is considered to assess the quality of the model obtained within the winning algorithm of the "READ//ABLE" competition, which focused on building an effective essay markup system.The results demonstrate that the new markup structure and evaluation approach provides a more comprehensive and accurate assessment of model performance, addressing the limitations of traditional metrics by accounting for complex markup scenarios and expert inconsistencies.