This map shows the geographic impact of Junya Honda'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 Junya Honda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junya Honda more than expected).
This network shows the impact of papers produced by Junya Honda. 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 Junya Honda. The network helps show where Junya Honda may publish in the future.
Co-authorship network of co-authors of Junya Honda
This figure shows the co-authorship network connecting the top 25 collaborators of Junya Honda.
A scholar is included among the top collaborators of Junya Honda 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 Junya Honda. Junya Honda is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Honda, Junya, et al.. (2020). Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions. 777–826.5 indexed citations
5.
Xu, Liyuan, Junya Honda, & Masashi Sugiyama. (2018). A fully adaptive algorithm for pure exploration in linear bandits.. International Conference on Artificial Intelligence and Statistics. 843–851.3 indexed citations
6.
Kato, Masahiro, et al.. (2018). Learning from Positive and Unlabeled Data with a Selection Bias. International Conference on Learning Representations.25 indexed citations
7.
Takeda, Akiko, et al.. (2018). Nonconvex Optimization for Regression with Fairness Constraints.. International Conference on Machine Learning. 2737–2746.22 indexed citations
8.
Honda, Junya, et al.. (2017). Position-based Multiple-play Bandit Problem with Unknown Position Bias. Neural Information Processing Systems. 30. 4998–5008.5 indexed citations
Honda, Junya, et al.. (2015). Regret lower bound and optimal algorithm in finite stochastic partial monitoring. Neural Information Processing Systems. 28. 1792–1800.1 indexed citations
13.
Honda, Junya & Akimichi Takemura. (2015). Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards. Journal of Machine Learning Research. 16(1). 3721–3756.2 indexed citations
14.
Honda, Junya & Akimichi Takemura. (2014). Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors. International Conference on Artificial Intelligence and Statistics. 375–383.8 indexed citations
15.
Uchida, Kazunori, et al.. (2013). Interpolation of communication distance in urban and suburban areas. International Symposium on Antennas and Propagation. 2. 873–876.3 indexed citations
16.
Honda, Junya & Akimichi Takemura. (2012). Stochastic Bandit Based on Empirical Moments. International Conference on Artificial Intelligence and Statistics. 529–537.1 indexed citations
17.
Honda, Junya & H. Yamamoto. (2012). Fast Linear-Programming decoding of LDPC codes over GF(2 m ). International Symposium on Information Theory and its Applications. 754–758.3 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.