Dmitry Kropotov
Researcher at Moscow State University
Email: kropotov -at- bayesgroup.ru
Research topics
- Sparse Bayesian Learning
- Variational Inference
- Bayesian Feature Design
- Textural Image Segmentation
- Deep Learning
Selected publications
- D. Elshin, D. Kropotov. MRF Energy Minimization Approach with Epitomic Textural Global Term for Image Segmentation Problems. In Proceedings of Bilateral Russian-Indian Workshop on Emerging Applications of Computer Vision, 2011 pdf
- D. Kropotov, D. Vetrov, L. Wolf, T. Hassner. Variational Relevance Vector Machine for Tabular Data. In Proceedings of 2nd Asian Conference on Machine Learning (ACML), 2010 pdf
- D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Variational Segmentation Algorithms with Label Frequency Constraints. Pattern Recognition and Image Analysis, Volume 20, Number 3, 324-334, 2010 link
- D. Kropotov, D. Vetrov. General Solutions for Information-Based and Bayesian Approaches to Model Selection in Linear Regression and Their Equivalence. Pattern Recognition and Image Analysis, Volume 19, Number 3, 447-455, 2009 link
- D.P. Vetrov, D.A. Kropotov, N.O. Ptashko. An Efficient Method for Feature Selection in Linear Regression Based on an Extended Akaike’s Information Criterion. Computational Mathematics and Mathematical Physics, Volume 49, Number 11, 1972-1985, 2009 link
- D. Kropotov, D. Vetrov. On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning. In Proceedings of 24th International Conference on Machine Learning (ICML), 2007 pdf
- D. Kropotov, D. Vetrov. Fuzzy Rules Generation Method for Pattern Recognition Problems. Lecture Notes in Computer Science (LNCS), Vol. 4578, 203–210, 2007 link
Projects
- Variational Optimization in Bayesian Models
Teaching
- Bayesian Methods in Machine Learning
- Graphical Models
- Mathematical Foundations of Prediction Theory
- Optimization in Machine Learning
Software
- Generalized Linear Models (MATLAB code)
- Variational Relevance Vector Machine for Tabular Data (paper, MATLAB code)
- Mixture of Normal Distributions (paper, MATLAB code)
Recent Publications
- MARS: Masked Automatic Ranks Selection in Tensor Decompositions (2023) AISTATS 2023
- Study on precoding optimization algorithms in massive MIMO system with multi-antenna users (2022)
- Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm (2022)
- A Randomized Coordinate Descent Method with Volume Sampling (2020)
- Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition (2018)
- A Superlinearly-Convergent Proximal Newton-Type Method for the Optimization of Finite Sums (2016)