2018| 2017| 2016| 2015| 2014| 2013

2018

TBA

2017

  1. Laugerotte, A., Zaytsev, A., Burnaev, E., Khominich, D., Pons, L., & Alestr, S. (2016). Datafusion for biological simulations: Application to toxins. Toxicon, 123, S52-S53.
  2. Burnaev E., Zaytsev A. Minimax approach to variable fidelity data interpolation. // Under review at AISTAT, 2017.
  3. Burnaev E., Zaytsev A. Large Scale Variable Fidelity Surrogate Modeling // Accepted in Annals of Mathematics and Artificial Intelligence, 2017.
  4. Burnaev E., Panin I., Sudret B. Effecient Design of Experiments for Sensitivity Analysis based on Polynomial Chaos Expansions Accepted in Annals of Mathematics and Artificial Intelligence, 2017.

2016

  1. Artemov, E. Burnaev. Optimal sequential estimation of a signal, observed in a fractional gaussian noise // Theory of Probability and Its Applications, 2016, vol. 60, № 1, pp. 126-134.
  2. E. Burnaev, P. Erofeev. The Influence of Parameter Initialization on the Training Time and Accuracy of a Nonlinear Regression Model, Journal of Communications Technology and Electronics, 2016, Vol. 61, No. 6, pp. 646–660.
  3. E. Burnaev, M. Belyaev, E. Kapushev. Computationally efficient algorithm for Gaussian Processes based regression in case of structured samples // Computational Mathematics and Mathematical Physics, 2016, Vol. 56, No. 4, pp. 499–513, 2016..
  4. E. Burnaev, M. Panov, A. Zaytsev. Regression on the Basis of Nonstationary Gaussian Processes with Bayesian Regularization, Journal of Communications Technology and Electronics, 2016, Vol. 61, No. 6, pp. 661–671.
  5. E. Burnaev, I. Panin, B. Sudret. Effective Design for Sobol Indices Estimation based on Polynomial Chaos Expansions // Lecture Notes in Artificial Intelligence, Vol. 9653, pp. 165-184, Springer, 2016.
  6. Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel Prikhodko, Dmitry Vetrov, Dmitry Yarotsky. GTApprox: Surrogate modeling for industrial design // Advances in Engineering Software 102 (2016) 29–39
  7. Sergey A. Evfratov, Ilya A. Osterman, Ekaterina S. Komarova, Alexandra M. Pogorelskaya, Maria P. Rubtsova, Timofei S. Zatsepin, Tatiana A. Semashko, Elena S. Kostryukova, Andrey A. Mironov, Evgeny Burnaev, Ekaterina Krymova, Mikhail S. Gelfand, Vadim M. Govorun, Alexey A. Bogdanov, Petr V. Sergiev and Olga A. Dontsova. Application of sorting and next generation sequencing to study 5’- UTR influence on translation efficiency in Escherichia coli // Nucleic Acids Research, 2016, 16 P. doi: 10.1093/nar/gkw1141
  8. Burnaev E, Nazarov I. Conformalized Kernel Ridge Regression // 15th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE Conference Publications, pp. 45 - 52, 2016. DOI: 10.1109/ICMLA.2016.0017
  9. Artemov A., Burnaev E. Detecting Performance Degradation of Software-Intensive Systems in the Presence of Trends and Long-Range Dependence // 16th International Conference on Data Mining Workshops (ICDMW), IEEE Conference Publications, pp. 29 - 36, 2016. DOI: 10.1109/ICDMW.2016.0013
  10. Burnaev E, Smolyakov D. One-Class SVM with Privileged Information and Its Application to Malware Detection // 16th International Conference on Data Mining Workshops (ICDMW), IEEE Conference Publications, pp. 273 - 280, 2016. DOI: 10.1109/ICDMW.2016.0046
  11. Evgeny Burnaev, Ivan Koptelov, German Novikov, Timur Khanipov. Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection // Proc. SPIE, Nineth International Conference on Machine Vision, 2016

2015

  1. E. Burnaev, S. Chernova. On an Iterative Algorithm for Calculating Weighted Principal Components // Journal of Communications Technology and Electronics, 2015, Vol. 60, No. 6, pp. 619–624.
  2. E. Burnaev, A. Zaytsev. Surrogate modeling of mutlifidelity data for large samples. Journal of Communications Technology and Electronics, 2015, Vol. 60, No. 12, pp. 1348–1355.
  3. M. Belyaev, E. Burnaev, and Y. Kapushev. Gaussian process regression for structured data sets. In Lecture Notes in Artificial Intelligence. Proceedings of SLDS 2015. A. Gammerman et al. (Eds.), volume 9047, pages 106–115, London, UK, April 20–23 2015. Springer.
  4. E. Burnaev and M. Panov. Adaptive design of experiments based on gaussian processes. In Lecture Notes in Artificial Intelligence. Proceedings of SLDS 2015. A. Gammerman et al. (Eds.), volume 9047, pages 116–126, London, UK, April 20–23 2015. Springer.
  5. E. Burnaev and I. Panin. Adaptive Design of Experiments for Sobol Indices Estimation Based on Quadratic Metamodel. In Lecture Notes in Artificial Intelligence. Proceedings of SLDS 2015. A. Gammerman et al. (Eds.), volume 9047, pages 86–96, London, UK, April 20–23 2015. Springer.
  6. E. Burnaev, P. Erofeev, D. Smolyakov. Model Selection for Anomaly Detection // Proc. SPIE 9875, Eighth International Conference on Machine Vision, 987525 (December 8, 2015); 5 P. doi:10.1117/12.2228794; http://dx.doi.org/10.1117/12.2228794
  7. Alexey Artemov, Evgeny Burnaev and Andrey Lokot. Nonparametric Decomposition of Quasi-periodic Time Series for Change-point Detection // Proc. SPIE 9875, Eighth International Conference on Machine Vision, 987520 (December 8, 2015); 5 P. doi:10.1117/12.2228370;http://dx.doi.org/10.1117/12.2228370
  8. Artemov A., Burnaev E. Ensembles of Detectors for Online Detection of Transient Changes // Proc. SPIE 9875, Eighth International Conference on Machine Vision, 98751Z (December 8, 2015); 5 P. doi:10.1117/12.2228369; http://dx.doi.org/10.1117/12.2228369
  9. E. Burnaev, P. Erofeev, A. Papanov. Influence of Resampling on Accuracy of Imbalanced Classification // Proc. SPIE9875, Eighth International Conference on Machine Vision, 987521 (December 8, 2015); 5 P. doi:10.1117/12.2228523; http://dx.doi.org/10.1117/12.2228523

2014

  1. E. Burnaev, V. Vovk. Efficiency of conformalized ridge regression. JMLR W&CP 35:605-622, 2014
  2. E. Burnaev, S. Alestra, P. Prikhodko, K. Sato. Construction of Low Dimensional Structures from Object Surface Description using Feature Extraction Technique // Journal of the Japan Society of Mechanical Engineers, 2014 / 5 / Vol 117 N° 1146, pp. 322 - 325 (in Japanese)    
  3. S. Alestra, E. Kapushev , M. Belyaev, E. Burnaev, M. Dormieux, A. Cavailles, D. Chaillot and E. Ferreira. Building Data Fusion Surrogate Models for Spacecraft Aerodynamic Problems with Incomplete Factorial Design of Experiments // Advanced Materials Research, Vol. 1016 (2014), pp. 405-412.
  4. S. Alestra, C. Bordry, C. Brand, E. Burnaev, P. Erofeev, A. Papanov and C. Silveira-Freixo. Application of Rare Event Anticipation Techniques to Aircraft Health Management // Advanced Materials Research, Vol. 1016 (2014), pp. 413-417.
  5. G. Sterling, P. Prikhodko, E. Burnaev, M. Belyaev and S. Grihon. On Approximation of Reserve Factors Dependency on Loads for Composite Stiffened Panels // Advanced Materials Research, Vol. 1016 (2014), pp. 85-89.
  6. A. Zaytsev, E. Burnaev, V. Spokoiny. Properties of the Bayesian Parameter Estimation of a Regression Based on Gaussian Processes // Journal of Mathematical Sciences, Volume 203, Issue 6, pp. 789-798, 15 Nov 2014.

2013

  1. Grihon S., Burnaev E.V., Belyaev M.G. and Prikhodko P.V. Surrogate Modeling of Stability Constraints for Optimization of Composite Structures // Surrogate-Based Modeling and Optimization. Engineering applications. Eds. by S. Koziel, L. Leifsson. Springer, 2013. P. 359-391.
  2. E. Burnaev, A. Zaytsev, V. Spokoiny. The Bernstein-von Mises theorem for regression based on Gaussian processes // Russ. Math. Surv. 68, No. 5, 954-956 (2013)
  3. E. Burnaev, P. Prikhod’ko. On a method for constructing ensembles of regression models // Automation and Remote Control, Volume 74, Issue 10, pp. 1630-1644, 12 Oct 2013.
  4. A. Zaitsev, E. Burnaev, V. Spokoiny.  Properties of the posterior distribution of a regression model based on Gaussian random fields // Automation and Remote Control, Volume 74, Issue 10, pp. 1645-1655, 12 Oct 2013.