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News
September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Proc. 35th European Conference on Information Retrieval (ECIR 2013): Advances in Information Retrieval

Vol. 7814. Springer, 2013.
Editor-in-chief: P. Serdyukov, P. Braslavski, Kuznetsov S., J. Kamps, S. M. Rüger, E. Agichtein, I. Segalovich, E. Yilmaz
Chapters
Topic models can improve domain term extraction
Bolshakova E. I., Loukachevitch N. V., Nokel M., , in: Proc. 35th European Conference on Information Retrieval (ECIR 2013): Advances in Information RetrievalVol. 7814.: Springer, 2013. P. 684–687.
The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study. ...
Added: November 18, 2013
A Web Mining Tool for Assistance with Creative Writing
Galitsky B., Kuznetsov S., , in: Proc. 35th European Conference on Information Retrieval (ECIR 2013): Advances in Information RetrievalVol. 7814.: Springer, 2013. P. 828–831.
Added: November 18, 2013
Topic Models Can Improve Domain Term Extraction
Elena Bolshakova, Natalia Loukachevitch, Nokel M., , in: Proc. 35th European Conference on Information Retrieval (ECIR 2013): Advances in Information RetrievalVol. 7814.: Springer, 2013. P. 684–687.
Abstract. The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study. ...
Added: October 1, 2014
Robust PLSA Performs Better Than LDA
Anna Potapenko, Konstantin Vorontsov, , in: Proc. 35th European Conference on Information Retrieval (ECIR 2013): Advances in Information RetrievalVol. 7814.: Springer, 2013. P. 784–787.
In this paper we introduce a generalized learning algorithm for probabilistic topic models (PTM). Many known and new algorithms for PLSA, LDA, and SWB models can be obtained as its special cases by choosing a subset of the following “options”: regularization, sampling, update frequency, sparsing and robustness. We show that a robust topic model, which ...
Added: December 23, 2014
Language: English
Keywords: information retrieval
Proc. 35th European Conference on Information Retrieval (ECIR 2013): Advances in Information Retrieval
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