Last week, Sydney, Australia hosted 34th International Conference on Machine Learning (ICML 2017), one of a few major machine learning conferences. Yandex team presented the result of the joint research project between Yandex,  Skolkovo Institute of Science and Technology and National Research University Higher School of Economics. In our paper "Variational Dropout Sparsifies Deep Neural Networks" (https://arxiv.org/abs/1701.05369), we introduced Sparse Variational Dropout, a simple yet effective technique for sparsification of neural networks.

Recently, at the conference WWW'2017 (26th International World Wide Web Conference), Alexey Drutsa has presented the paper "Horizon-Independent Optimal Pricing in Repeated Auctions with Truthful and Strategic Buyers". We will briefly explain the core of this study.

In early 2016, at the conference WSDM'2017 (The 10th ACM International Conference on Web Search and Data Mining), Eugene Kharitonov, Alexey Drutsa, and Pavel Serdyukov have presented the paper "Learning Sensitive Combinations of A/B Test Metrics". We will  briefly explain its essence.