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Konstantin Vorontsov

@konstantin_vorontsov

Machine learning, topic modelling; MSU CMC, MIPT, FRC CSC RAS

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About

Konstantin Vyacheslavovich Vorontsov is a Professor of the Russian Academy of Sciences and a Doctor of Physical and Mathematical Sciences. He is Head of the Department of Mathematical Methods of Forecasting at the Faculty of Computational Mathematics and Cybernetics (CMC) of Moscow State University, where he has been a professor since 2021; Head of the Laboratory «Machine Learning and Semantic Analysis» of the MSU Institute for Artificial Intelligence (since 2022); Head of the Department of Machine Learning and Digital Humanities at MIPT (since 2021); Head of the Department of Intelligent Systems at MIPT (since 2022), where he has worked since 2004; and Chief Research Scientist of the Intelligent Systems Division of the Dorodnicyn Computing Centre, FRC CSC RAS, where he has worked since 2001.

He graduated from MIPT (1994) and completed postgraduate study at the Computing Centre of the RAS (1997). His Candidate of Sciences thesis is «Local bases in the algebraic approach to the recognition problem» (1999, speciality 01.01.09); his Doctor of Sciences thesis is «The combinatorial theory of reliability of learning from examples» (2010, speciality 05.13.17).

He is the author of the combinatorial theory of reliability of learning from examples, which bounds the probability of overfitting taking into account the splitting and similarity effects in families of algorithms; his main line of research is the theory and methods of additive regularization of topic models (ARTM). Since 2014 he has been the scientific lead of the open-source topic modelling library BigARTM, and since 2019 of the search and recommendation system SciSearch. He is one of the founders and administrators of the MachineLearning.ru resource (since 2008).

RAS Medal with a prize for young scientists (1999, jointly with E. V. Naryzhny); honorary title of Professor of the RAS (2015); RAS jubilee medal «300 Years of the Russian Academy of Sciences» (2024); Yandex ML Prize, «Hall of Fame» nomination (2025).

He is the author of the courses «Probabilistic Topic Models», «Mathematical Methods of Text Analysis», «Applied Statistical Data Analysis» and a special course on the theory of reliability of learning from examples; in 2008–2020 he taught at the Yandex School of Data Analysis. Since 2004 he has taught the year-long course «Machine Learning» at MIPT and MSU CMC.

Personal Information

Current Positions

Заведующий кафедрой «Математические методы прогнозирования», ВМК МГУ

Заведующий лабораторией «Машинное обучение и семантический анализ», Институт искусственного интеллекта МГУ

Заведующий кафедрой «Машинное обучение и цифровая гуманитаристика», МФТИ

Главный научный сотрудник отдела «Интеллектуальные системы», ВЦ ФИЦ ИУ РАН

Researcher Info

Web of Science ResearcherID

G-7857-2014

Scopus Author ID

6507982932
Most popular works
All works

Machine Learning 2. Lecture 10. Reinforcement Learning

Aug 30, 2026LectureКонстантин Вячеславович Воронцов

Reinforcement learning as learning from the environment's response: the multi-armed bandit problem, estimating the value of an action, the greedy and ε-greedy strategies, the exploration–exploitation trade-off, the UCB method, the exponential moving average, the pursuit method and reinforcement comparison; an environment with states, the Markov decision process, how it differs from ordinary machine learning problems, the discounted return, the state and action value functions, the Bellman equation, the SARSA and Q-learning methods, on-policy and off-policy; deep Q-learning DQN on the example of Atari games, experience replay; gradient optimisation of the policy (policy gradient) and its connection with log-likelihood maximisation; model-based and model-free approaches, a regression model of the reward with an upper confidence bound, LinUCB, recommending news articles, evaluating a policy on historical data.
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Machine Learning 1. Lecture 14. Anomaly Detection and Robust Methods

Aug 30, 2026LectureКонстантин Вячеславович Воронцов

Anomaly and novelty detection; heuristic anomaly scores: local density, isolation forest, a mixture with a background component, robust principal component analysis; outlier removal in nonparametric regression, the LOWESS algorithm; anomaly as the value of the loss function; the theory of robust learning: slowly growing functions, iterative reweighting of objects with IRS and IRLS, robust loss functions, the median, the quantile and the censored mean, robust aggregation functions and their smoothing, the IR-ERM algorithm; one-class SVM, learning from positive and unlabeled objects (PU-learning), problems with new and unknown classes.
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Machine Learning 2. Lecture 4. Attention Models and Transformers

Aug 30, 2026LectureКонстантин Вячеславович Воронцов

Sequence-to-sequence problems: the recurrent network, the encoder–decoder architecture and its limitations; the recurrent network with an attention model, interpreting the attention matrix in machine translation and image captioning; similarity functions, the query–key–value attention model, the attention formula, self-attention; multi-head attention and its regularisation, hierarchical attention for document classification, the graph attention model GAT; the transformer for machine translation: the encoder, positional encoding, layer normalisation and skip connections, the decoder with masking, the training criterion, BLEU and WER; the transformer encoder BERT, the MLM and NSP criteria, fine-tuning, BPE and WordPiece tokenisation, model sizes.
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Recent works

Machine Learning. Additional Lecture 2. A Survey of Optimisation Problems in Machine Learning

Aug 30, 2026LectureКонстантин Вячеславович Воронцов

A survey lecture: machine learning as formulations of optimisation problems, a model, a loss function, a regulariser. Regression and classification, continuous approximations of the threshold loss function, SVM and logistic regression, quantile, robust and SVM regression; the L0, L1, L2, Elastic Net, SFM, RFM regularisers and feature selection; learning to rank: ordinal regression, the pairwise approach and AUC, LambdaRank; estimating a density and a mixture of distributions, clustering as a special case of a mixture, the one-class SVM; semi-supervised learning, the transductive SVM, PU-learning; matrix factorisations, autoencoders, multidimensional scaling, graph factorisations, GraphEDM; transfer learning, multi-task and self-supervised learning, distillation, learning using privileged information, generative adversarial networks; a typology of machine learning problems.
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Machine Learning. Additional Lecture 1. The Theory of Overfitting

Aug 30, 2026LectureКонстантин Вячеславович Воронцов

The combinatorial theory of overfitting: the probability of overfitting and why the Vapnik–Chervonenkis bounds are loose; experiments with overfitting — a monotone chain of algorithms, the effect of splitting and connectivity of the family on the probability of overfitting and on the complete cross-validation estimate, overfitting of chains, the exact formula for the probability of overfitting when choosing between two algorithms; the splitting–connectivity graph of a family of algorithms, examples for linear classifiers, a monotone chain and a two-dimensional net; generating and forbidden sets, upper connectivity and inferiority of an algorithm; the splitting–connectivity upper bound, the idea of its proof and its properties.
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Machine Learning 2. Lecture 12. Interpretability and Explainability

Aug 30, 2026LectureКонстантин Вячеславович Воронцов

Interpretability and explainability of machine learning models: goals, tasks and basic notions, who needs explanations and why; interpretable models; feature importance in linear regression: the t-statistic, net effects, contributions and situational importance; partial dependence plots (PDP) and individual conditional expectation plots (ICE); permutation feature importance; the Shapley value from cooperative game theory, its axioms and Monte Carlo estimation, its use in linear regression; surrogate modelling in the neighbourhood of an object, the LIME, Anchors, SHAP, Shapley Kernel and SAGE methods; the Shapley value for training objects, Data Shapley and Gradient Shapley; counterfactual explanations.
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