Publications

Preprints

1. Fursov, I., et al. “Adversarial Attacks on Deep Models for Financial Transaction Records.” arXiv preprint arXiv:2106.08361 (2021).
2. Romanenkova, E., et al. “Principled change point detection via representation learning.” arXiv preprint arXiv:2106.02602. 2021.
3. Zhuzhel, V., et al.  COHORTNEY: Deep Clustering for Heterogeneous Event Sequences. arXiv preprint arXiv:2104.01440. 2021.
4. I. Fursov, A. Zaytsev et al. Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers. arXiv preprint arXiv:2006.11078. 2020.
5. R. Kail, A. Zaytsev, E. Burnaev. Recurrent Convolutional Neural Networks help to predict location of Earthquakes. arXiv preprint arXiv:2004.09140. 2020.
6. I. Fursov, A. Zaytsev et al. Sequence embeddings help to identify fraudulent cases in healthcare insurance. arXiv preprint arXiv:1910.03072. 2019.
7. S. Kumbhakar, A. Peresetsky, Y. Shchetynin, A. Zaytsev. Technical efficiency and inefficiency: SFA misspecification. https://arxiv.org/abs/1902.02824. 2019.
8. L. Matyushin, A. Zaytsev. Multifidelity Bayesian Optimization for Binomial Output. arXiv preprint arXiv:1902.06937. 2019.

2020

1. E. Romanenkova, A. Zaytsev et al. Real-time data-driven detection of the rock type alteration during directional drilling. IEEE Geoscience and Remote Sensing Letters. 2020.
2. E. Gurina et al. Application of machine learning to accident detection at directional drilling. Journal of Petroleum Science and Engineering. 2020.
3. S. Kumbhakar, A. Peresetsky et al. Technical efficiency, and inefficiency: Reassurance of standard SFA models and a misspecification problem. MPRA Paper No. 102797. 2020.
4. V. Snorovikhina, A. Zaytsev. Unsupervised anomaly detection for discrete sequence healthcare data. arXiv preprint arXiv:2007.10098. 2020.
5. I. Fursov, A. Zaytsev et al. Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world. arXiv preprint arXiv:2003.04173. 2020.

2019

1. P. Proskura, A. Zaytsev et al. Usage of multiple RTL features for Earthquake prediction. ICCSA 2019: Computational Science and Its Applications – ICCSA 2019. pp 556-565.
2. N. Klyuchnikov, A. Zaytsev et al. Data-driven model for the identification of the rock type at a drilling bit. Journal of Petroleum Science and Engineering. 2020.
3. K. Antipova, N. Klyuchnikov et al. Data-Driven Model for the Drilling Accidents Prediction. SPE Annual Technical Conference and Exhibition. Paper No. SPE-195888-MS. 2019.

2018

1. A. Zaytsev, D. Ermilov, E. Romanenkova. Interpolation error of Gaussian process regression for misspecified case. COPA

2017

1. A. Baranov, E. Burnaev et al. Optimising the Active Muon Shield for the SHiP Experiment at CERN. 3rd International Conference on Particle Physics and Astrophysics, ICPPA 2017. Volume 934, Issue 1.
2. N.Kozlovskaya, A.Zaytsev. Deep Ensembles for Imbalanced Classification. ICMLA.
3. A. Zaytsev, E. Burnaev. Minimax Approach to Variable Fidelity Data Interpolation. AISTATS.
4. A. Zaytsev, E. Burnaev. Large scale variable fidelity surrogate modeling. Annals of Mathematics and Artificial Intelligence

2016

1. A. Laugerotte, A. Zaytsev, E. Burnaev, D. Khominich, L. Pons, S. Alestra Data fusion for biological simulations: Application to toxins. Toxicon 123.
2. A. Zaytsev. Reliable surrogate modeling of engineering data with more than two levels of fidelity. Mechanical and Aerospace Engineering (ICMAE), 2016 7th International Conference on. pp. 341-345.
3. E. Burnaev, M. Panov, A. Zaytsev. Regression on the basis of nonstationary Gaussian processes with Bayesian regularization Journal of Communications Technology & Electronics V. 61, N. 6. P. 661.
4. A. Zaytsev. Variable Fidelity Regression Using Low Fidelity Function Blackbox and Sparsification. Symposium on Conformal and Probabilistic Prediction with Applications. PP. 147-164.
5. E.V. Burnaev, A.A. Zaytsev. Surrogate modeling of multifidelity data for large samples. Journal of Communications Technology & Electronics. V. 60 (12). P. 1348.

2014

1. A.A. Zaytsev, E.V. Burnaev, V.G. Spokoiny. Properties of the Bayesian parameter estimation of a regression based on Gaussian processes. J. Math. Sci. V. 203 (6). PP. 789-798.

2013

1. E.V. Burnaev, A.A. Zaytsev, V.G. Spokoiny. The Bernstein-von Mises theorem for regression based on Gaussian Processes. Russ. Math. Surv. V. 68 (5). PP. 954-956.
2. A.A. Zaytsev, E.V. Burnaev, V.G. Spokoiny. Properties of the posterior distribution of a regression model based on Gaussian random fields. Automation and Remote Control. V. 74 (10). PP. 1645-1655.
3. A. Zaytsev, E. Burnaev, V. Spokoiny. Properties of posterior distribution of parameters for Gaussian processes regression Conference on Structural Inference in Statistics.