Our Members’ Publications:

Machine Learning

  1. Artemenkov, A., Panov, M.  (2020). NCVis: Noise Contrastive Approach for Scalable Visualization. In Proceedings of the Web Conference, pp. 2941-2947.
  2. Belyaev, M., Burnaev, E., Kapushev, E., Panov, M., Prikhodko, P., Vetrov, D., & Yarotsky, D. (2016). GTApprox: Surrogate modeling for industrial design. Advances in Engineering Software, 102, 29–39.
  3. Burnaev, E. V, Panov, M. E., & Zaytsev, A. A. (2016). Regression on the basis of nonstationary Gaussian processes with Bayesian regularization. Journal of Communications Technology and Electronics, 61(6), 661–671
  4. Burnaev, E., & Panov, M.(2015). Adaptive design of experiments based on gaussian processes. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9047, pp. 116–125.
  5. Gomtsyan, M., Mokrov, N., Panov, M., & Yanovich, Y. (2019). Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension. Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR, 101, 1126-1141.
  6. Panov, M., Tatarchuk, A., Mottl, V., & Windridge, D. (2011, June). A modified neutral point method for kernel-based fusion of pattern-recognition modalities with incomplete data sets. In International Workshop on Multiple Classifier Systems (pp. 126-136). Springer, Berlin, Heidelberg.
  7. Panov, M. & Tsepa, S. (2019).Constructing graph node embeddings via discrimination of similarity distributions. IEEE International Conference on Data Mining Workshops, ICDMW, 2018, 1050–1053.
  8. Slavinov, K., & Panov, M. (2018). Overlapping community detection in weighted graphs: Matrix factorization approach. In International Conference on Intelligent Data Processing: Theory and Applications (pp. 3-14). Springer, Cham.
  9. Tsymbalov, E., Makarychev, S., Shapeev, A., & Panov, M. (2019). Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning.  In 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Macao, China.
  10. Tsymbalov, E., Panov, M., & Shapeev, A. (2018). Dropout-based active learning for regressionLecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 247–258.


  1. Panov, M. E. (2016). Nonasymptotic approach to Bayesian semiparametric inference. Doklady Mathematics, 93(2), 155–158.
  2. Panov, M. & Spokoiny, V. (2015). Finite sample Bernstein-von Mises theorem for semiparametric problems.Bayesian Analysis, 10(3), 665–710.
  3. Panov, M. E. & Spokoiny, V. G. (2014). Critical dimension in the semiparametric Bernstein—von Mises theorem. Proceedings of the Steklov Institute of Mathematics287(1), 232-255.
  4. Panov, M., Slavnov, K., & Ushakov, R.(2018). Consistent estimation of mixed memberships with successive projections. Studies in Computational Intelligence, Vol. 689, pp. 53–64.


  1. Fedorov, F. S., Vasilkov, M. Y., Panov, M., Rupasov, D., Rashkovskiy, A., Ushakov, N. M., Nasibulin, A. G. (2019). Tailoring electrochemical efficiency of hydrogen evolution by fine-tuning of TiOx/RuOcomposite cathode architecture. International Journal of Hydrogen Energy, 44(21), 10593–10603.
  2. Menshchikov, A., Ermilov, D., Dranitsky, I., Kupchenko, L., Panov, M., Fedorov, M., Somov A. (2019). Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and GesturesIECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, 5602-5609.
  3. Ermilov, D., Panov, M., & Yanovich, Y. (2018). Automatic bitcoin address clustering. Proceedings – 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2018, 461–466.
  4. Mokrov, N., Panov, M., Gutman, B. A., Faskowitz, J. I., Jahanshad, N., & Thompson, P. M. (2018). Simultaneous matrix diagonalization for structural brain networks classificationStudies in Computational Intelligence, Vol. 689, pp. 1261–1270.


  • Nazarov, I., Burkina, M., Shirokikh, B., Fedonin, G., Panov, M. (2019). Sparse-group inductive matrix completion. Submitted to Journal of Computational Mathematics and Mathematical Physics
  • Shumovskaia, V., Fedyanin, K., Sukharev, I., Berestnev, D., Panov, M. (2020). Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data. arXiv preprint arXiv:2001.08427
  • Spokoiny, V., Panov, M. (2019). Accuracy of Gaussian approximation in nonparametric Bernstein–von Mises Theorem.ArXiv preprint arXiv:1910.06028
  • Thin, A., Kotelevskii, N., Denain, J.-S., Grinsztajn, L., Durmus, A., Panov, M., Moulines, E. (2020). MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. arXiv preprint arXiv:2002.12253
  • Tsymbalov, E.  Fedyanin, K., Panov, M. (2020). Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles. arXiv preprint arXiv:2003.03274