Maxim Kaledin
- Research Assistant, Doctoral Student:Faculty of Computer Science / International Laboratory of Stochastic Algorithms and High-Dimensional Inference
- Maxim Kaledin has been at HSE University since 2015.
Postgraduate Studies
1st year of study
Approved topic of thesis: Uncertainty quantification in reinforcement learning
Academic Supervisor: Belomestny, Denis
Courses (2019/2020)
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
- Numerical Methods (Bachelor’s programme; Faculty of Computer Science; 3 year, 3, 4 module)Rus
- Past Courses
Publications2
- Chapter Naumov A., Moulines E., Kaledin M., Tadic V., Wai H. Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise, in: Proceedings of Machine Learning Research Vol. 125: Proceedings of Thirty Third Conference on Learning Theory. , 2020. P. 2144-2203.
- Article Belomestny D., Kaledin M., Schoenmakers J. Semitractability of optimal stopping problems via a weighted stochastic mesh algorithm // Mathematical Finance. 2020 doi
First Cohort Graduates from Master’s Programme in Statistical Learning Theory
The Master's Programme in Statistical Learning Theory was launched in 2017. It is run jointly with the Skolkovo Institute of Science and Technology (Skoltech). The programme trains future scientists to effectively carry out fundamental research and work on new challenging problems in statistical learning theory, one of the most promising fields of science. Yury Kemaev and Maxim Kaledin, from the first cohort of programme graduates, sat down with HSE News Service to talk about their studies and plans for the future.
First Cohort Graduates from Master’s Programme in Statistical Learning Theory
The Master's Programme ‘Statistical Learning Theory’ was launched in 2017, and is run jointly with the Skolkovo Institute of Science and Technology(Skoltech).
