Igor Labutov

1.3k total citations · 1 hit paper
26 papers, 674 citations indexed

About

Igor Labutov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications. According to data from OpenAlex, Igor Labutov has authored 26 papers receiving a total of 674 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 4 papers in Computer Science Applications. Recurrent topics in Igor Labutov's work include Natural Language Processing Techniques (12 papers), Topic Modeling (11 papers) and Intelligent Tutoring Systems and Adaptive Learning (5 papers). Igor Labutov is often cited by papers focused on Natural Language Processing Techniques (12 papers), Topic Modeling (11 papers) and Intelligent Tutoring Systems and Adaptive Learning (5 papers). Igor Labutov collaborates with scholars based in United States, Israel and China. Igor Labutov's co-authors include Thorsten Joachims, David Mimno, Tobias Schnabel, Hod Lipson, Lucy Vanderwende, Sumit Basu, Shashank Srivastava, Daqing He, Peter Brusilovsky and Tom M. Mitchell and has published in prestigious journals such as Machine Vision and Applications, Autonomous Agents and Multi-Agent Systems and International Journal of Artificial Intelligence in Education.

In The Last Decade

Igor Labutov

26 papers receiving 611 citations

Hit Papers

Evaluation methods for unsupervised word embeddings 2015 2026 2018 2022 2015 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Evaluation methods for unsupervised word embeddings
    2015 319 citations Tobias Schnabel, Igor Labutov et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Igor Labutov United States 11 556 121 84 40 39 26 674
Mrinmaya Sachan Switzerland 16 733 1.3× 194 1.6× 96 1.1× 29 0.7× 35 0.9× 93 879
Eric K. Ringger United States 18 712 1.3× 131 1.1× 59 0.7× 31 0.8× 37 0.9× 61 829
Wenliang Dai Hong Kong 9 445 0.8× 129 1.1× 80 1.0× 15 0.4× 27 0.7× 18 691
John Hewitt United States 8 771 1.4× 183 1.5× 86 1.0× 38 0.9× 11 0.3× 11 975
Claire Gardent France 17 1.1k 2.0× 139 1.1× 108 1.3× 62 1.6× 14 0.4× 110 1.3k
Iulian Vlad Serban Canada 10 977 1.8× 201 1.7× 87 1.0× 14 0.3× 43 1.1× 14 1.1k
Wei-Yun Ma Taiwan 12 664 1.2× 72 0.6× 125 1.5× 56 1.4× 16 0.4× 42 784
Stella Biderman United States 7 565 1.0× 98 0.8× 119 1.4× 16 0.4× 18 0.5× 15 720
Ian Tenney United States 11 791 1.4× 186 1.5× 102 1.2× 38 0.9× 9 0.2× 13 951
Chandra Bhagavatula United States 14 1.0k 1.9× 263 2.2× 150 1.8× 47 1.2× 16 0.4× 38 1.2k

Countries citing papers authored by Igor Labutov

Since Specialization
Citations

This map shows the geographic impact of Igor Labutov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Igor Labutov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Igor Labutov more than expected).

Fields of papers citing papers by Igor Labutov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Igor Labutov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Igor Labutov. The network helps show where Igor Labutov may publish in the future.

Co-authorship network of co-authors of Igor Labutov

This figure shows the co-authorship network connecting the top 25 collaborators of Igor Labutov. A scholar is included among the top collaborators of Igor Labutov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Igor Labutov. Igor Labutov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Labutov, Igor, et al.. (2020). Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education. 31(4). 820–846. 17 indexed citations
2.
Azaria, Amos, Shashank Srivastava, Jayant Krishnamurthy, Igor Labutov, & Tom M. Mitchell. (2019). An agent for learning new natural language commands. Autonomous Agents and Multi-Agent Systems. 34(1). 4 indexed citations
3.
Srivastava, Shashank, Igor Labutov, & Tom M. Mitchell. (2019). Learning to Ask for Conversational Machine Learning. 4162–4172. 1 indexed citations
4.
Labutov, Igor, et al.. (2019). Look-up and Adapt: A One-shot Semantic Parser. 1129–1139. 3 indexed citations
5.
Srivastava, Shashank, et al.. (2018). Zero-shot Learning of Classifiers from Natural Language Quantification. 306–316. 29 indexed citations
6.
Srivastava, Shashank, et al.. (2017). Joint Concept Learning and Semantic Parsing from Natural Language Explanations. 1527–1536. 29 indexed citations
7.
Labutov, Igor, Yun Huang, Peter Brusilovsky, & Daqing He. (2017). Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. 907–915. 18 indexed citations
8.
Labutov, Igor & Christoph Studer. (2017). JAG: A Crowdsourcing Framework for Joint Assessment and Peer Grading. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 6 indexed citations
9.
Labutov, Igor & Hod Lipson. (2016). Web as a Textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web.. Educational Data Mining. 110–118. 7 indexed citations
10.
Labutov, Igor, et al.. (2016). A Queueing Network Model for Spaced Repetition. 289–292. 2 indexed citations
11.
Labutov, Igor, et al.. (2016). Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation. 93–96. 16 indexed citations
12.
Labutov, Igor, Sumit Basu, & Lucy Vanderwende. (2015). Deep Questions without Deep Understanding. 889–898. 69 indexed citations
13.
Schnabel, Tobias, Igor Labutov, David Mimno, & Thorsten Joachims. (2015). Evaluation methods for unsupervised word embeddings. 298–307. 319 indexed citations breakdown →
14.
Labutov, Igor & Hod Lipson. (2013). Re-embedding words. Meeting of the Association for Computational Linguistics. 489–493. 55 indexed citations
15.
Labutov, Igor & Hod Lipson. (2012). Humor as Circuits in Semantic Networks. Meeting of the Association for Computational Linguistics. 150–155. 19 indexed citations
16.
17.
Labutov, Igor, et al.. (2011). Generating near-spherical range panoramas by fusing optical flow and stereo from a single-camera folded catadioptric rig. Machine Vision and Applications. 24(1). 133–144. 8 indexed citations
18.
19.
Labutov, Igor, et al.. (2009). Learning to stabilize the head of a quadrupedal robot with an artificial vestibular system. 79. 2512–2517. 12 indexed citations
20.
Labutov, Igor, et al.. (2008). Combinning linear vestibulo-ocular and opto-kinetic reflex in a humanoid robot. 79. 132–137. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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