PhD Student at Skoltech
Alexander is a PhD student at Skolkovo Institute of Science and Technology under Prof. Ivan Oseledets supervision in collaboration with Yandex company. His current research interests are focused on applications of low-rank data approximation methods to machine learning and information retrieval problems. He has started to work on topics related to machine learning at Mathematical Methods of Forecasting department of Lomonosov Moscow State University under Prof. Alexander Dyakonov supervision and at Computer Science Department of Yandex School of Data Analysis. Moreover he has professional experience in natural language processing, industrial programming and large-scale data processing due to his work at Machine Translation Department of Yandex company.
Machine learning, data mining, information retrieval, low-rank approximations, natural language processing, ensembles of predictors, gradient boosting, categorical variables in machine learning, matrix and tensor factorizations, matrix and tensor completion, optimization, recommender systems, language modeling
Web site: http://newo.su
All publications, sorted by year
- Alexander Fonarev, Oleksii Hrinchuk, Gleb Gusev, Pavel Serdyukov, and Ivan Oseledets. Riemannian optimization for skip-Gram negative sampling. arXiv preprint 1704.08059, 2017. URL: http://arxiv.org/abs/1704.8059. [ bib ]
- Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, and Ivan Oseledets. Efficient rectangular maximal-volume algorithm for rating elicitation in collaborative filtering. arXiv preprint 1610.04850, 2016. accepted at ICDM 2016. URL: http://arxiv.org/abs/1610.04850. [ bib ]