Kirill Struminsky
- Postgraduate Student, Visiting Scholar:Faculty of Computer Science / Big Data and Information Retrieval School
- Research Assistant:Faculty of Computer Science / Big Data and Information Retrieval School / Centre of Deep Learning and Bayesian Methods
- Kirill Struminsky has been at HSE since 2016.
Responsibilities
To conduct research in the area of deep generative modeling and related areas of deep learning.
Postgraduate Studies
3rd year of study
Approved topic of thesis: Continuous optimization methods in design of error detection and correction schemes
Academic Supervisor: Vetrov, Dmitry
Courses (2018/2019)
- Research Seminar "Machine Learning and Applications" (Bachelor’s programme; Faculty of Computer Science; programme "Applied Mathematics and Information Science"; 3 year, 1-4 module)Rus
- Past Courses
Courses (2016/2017)
Research seminar "Machine Learning and Applications" (Bachelor’s programme; Faculty of Computer Science; 3 year, 1-4 module)Rus
Publications4
- Chapter Struminsky K., Lacoste-Julien S., Osokin A. Quantifying Learning Guarantees for Convex but Inconsistent Surrogates, in: Advances in Neural Information Processing Systems 31 (NIPS 2018). , 2018. (in press)
- Article Фигурнов М. В., Струминский К. А., Ветров Д. П. Устойчивый к шуму метод обучения вариационного автокодировщика // Интеллектуальные системы. Теория и приложения. 2017. Т. 21. № 2. С. 90-109.
- Chapter Struminsky K., Kruglik S., Vetrov D., Oseledets I. A new approach for sparse Bayesian channel estimation in SCMA uplink systems, in: 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016. October 13 - 15, Yangzhou, China. NY : Institute of Electrical and Electronic Engineers, 2016. P. 1-5. doi
- Preprint Figurnov M., Struminsky K., Vetrov D. Robust Variational Inference / Cornell University. Series arXiv:1611.09226 "arxiv.org". 2016.
'Machine Learning Algorithm Able to Find Data Patterns a Human Could Not'
In December 2016, five new international laboratories opened up at the Higher School of Economics, one of which was the International Laboratory of Deep Learning and Bayesian Methods. This lab focuses on combined neural Bayesian models that bring together two of the most successful paradigms in modern-day machine learning – the neural network paradigm and the Bayesian paradigm.
