Yuta Tsuboi

1.0k total citations
21 papers, 441 citations indexed

About

Yuta Tsuboi is a scholar working on Artificial Intelligence, Information Systems and Computer Science Applications. According to data from OpenAlex, Yuta Tsuboi has authored 21 papers receiving a total of 441 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 4 papers in Information Systems and 4 papers in Computer Science Applications. Recurrent topics in Yuta Tsuboi's work include Natural Language Processing Techniques (9 papers), Topic Modeling (7 papers) and Anomaly Detection Techniques and Applications (5 papers). Yuta Tsuboi is often cited by papers focused on Natural Language Processing Techniques (9 papers), Topic Modeling (7 papers) and Anomaly Detection Techniques and Applications (5 papers). Yuta Tsuboi collaborates with scholars based in Japan, United States and Germany. Yuta Tsuboi's co-authors include Hisashi Kashima, Shohei Hido, Masashi Sugiyama, Takafumi Kanamori, Steffen Bickel, Hiroki Ouchi, Yūji Matsumoto, Issei Sato, Yuya Unno and Hiroshi Kanayama and has published in prestigious journals such as Journal of Chemical Theory and Computation, Faraday Discussions and Knowledge and Information Systems.

In The Last Decade

Yuta Tsuboi

19 papers receiving 405 citations

Peers

Countries citing papers authored by Yuta Tsuboi

Since Specialization

Citations

This map shows the geographic impact of Yuta Tsuboi'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 Yuta Tsuboi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuta Tsuboi more than expected).

Fields of papers citing papers by Yuta Tsuboi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuta Tsuboi. 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 Yuta Tsuboi. The network helps show where Yuta Tsuboi may publish in the future.

Co-authorship network of co-authors of Yuta Tsuboi

This figure shows the co-authorship network connecting the top 25 collaborators of Yuta Tsuboi. A scholar is included among the top collaborators of Yuta Tsuboi 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 Yuta Tsuboi. Yuta Tsuboi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Generative Model for Constructing Reaction Path from Initial to Final States 2025 ·Journal of Chemical Theory and Computation ·Akihide Hayashi, So Takamoto, Ju Li, Yuta Tsuboi, Daisuke Okanohara	0
2	A Convex Formulation for Learning from Crowds 2021 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Yuta Tsuboi, Hisashi Kashima	11
3	Addressee and Response Selection for Multi-Party Conversation 2016 ·Hiroki Ouchi, Yuta Tsuboi	28
4	New crystalline complex metal oxides created by unit-synthesis and their catalysis based on porous and redox properties 2016 ·Faraday Discussions ·Zhenxin Zhang, Satoshi Ishikawa, Yuta Tsuboi, Masahiro Sadakane, Toru Murayama, Wataru Ueda	11
5	Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning 2014 ·Hiroshi Kanayama, Youngja Park, Yuta Tsuboi, (unknown)	3
6	Neural Networks Leverage Corpus-wide Information for Part-of-speech Tagging 2014 ·Yuta Tsuboi	19
7	Clustering crowds 2013 ·National Conference on Artificial Intelligence ·(unknown), Yuta Tsuboi, Hisashi Kashima	14
8	Learning from Crowds and Experts 2013 ·Transactions of the Japanese Society for Artificial Intelligence ·(unknown), Yuta Tsuboi, Issei Sato, Hisashi Kashima	18
9	Clustering Crowds 2013 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Yuta Tsuboi, Hisashi Kashima	6
10	Syntactic Difference Based Approach for NTCIR-9 RITE Task. 2011 ·NTCIR ·Yuta Tsuboi, Hiroshi Kanayama, (unknown), Yuya Unno	4
11	Fast Newton-CG Method for Batch Learning of Conditional Random Fields 2011 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Yuta Tsuboi, Yuya Unno, Hisashi Kashima, Naoaki Okazaki	2
12	Feature-Rich Information Extraction for the Technical Trend-Map Creation. 2010 ·NTCIR ·(unknown), Yuta Tsuboi, Yuya Unno, Hironori Takeuchi	4
13	Statistical outlier detection using direct density ratio estimation 2010 ·Knowledge and Information Systems ·Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori	111
14	Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation 2009 ·Journal of Information Processing ·Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama	58
15	Inlier-Based Outlier Detection via Direct Density Ratio Estimation 2008 ·Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori	47
16	Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation 2008 ·Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama	34
17	Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs 2004 ·Hisashi Kashima, Yuta Tsuboi	9
18	Learning sequence-to-sequence correspondences from parallel corpora via sequential pattern mining 2003 ·Kaoru Yamamoto, Taku Kudo, Yuta Tsuboi, Yūji Matsumoto	7
19	Mining Syntactic Structures from Text Database 2002 ·Medical Entomology and Zoology ·Taku Kudo, Kaoru Yamamoto, Yuta Tsuboi, Yūji Matsumoto	1
20	A method for detecting and eliminating feature interactions using a frame model 2002 ·Masahide Nakamura, Yuta Tsuboi	1

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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