Aleksandr Vorobev

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Online evaluation methods, such as A/B and interleaving experiments, are widely used for search engine evaluation. Since they rely on noisy implicit user feedback, running each experiment takes a considerable time. Recently, the problem of reducing the duration of online experiments has received substantial attention from the research community. However,(More)
Given a repeatedly issued query and a document with a not-yet-confirmed potential to satisfy the users' needs, a search system should place this document on a high position in order to gather user feedback and obtain a more confident estimate of the document utility. On the other hand, the main objective of the search system is to maximize expected user(More)
While gradient boosting algorithms are the workhorse of modern industrial machine learning and data science, all current implementations are susceptible to a nontrivial but damaging form of label leakage. It results in a systematic bias in pointwise gradient estimates that lead to reduced accuracy. This paper formally analyzes the issue and presents(More)
A relationship between the qualitative and quantitative characteristics of representatives of the normal microflora in biocenosis of the colonic lumen (CL) was studied in 18 patients with subacute bacterial endocarditis, 18 patients with rheumatic heart disease, 13 with chronic renal failure and 50 healthy individuals without clinical signs of(More)
Implicit user feedback is known to be a strong signal of user preferences in web search. Hence, solving the explorationexploitation dilemma [5] became an important direction of improvement of ranking algorithms in the last years. In this poster, in the case of commercial queries, we consider a new negative effect of exploration on the user utility –(More)