Citations per year, relative to Akiko Takeda Akiko Takeda (= 1×)
peers
Liwei Zhang
Countries citing papers authored by Akiko Takeda
Since
Specialization
Citations
This map shows the geographic impact of Akiko Takeda'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 Akiko Takeda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akiko Takeda more than expected).
This network shows the impact of papers produced by Akiko Takeda. 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 Akiko Takeda. The network helps show where Akiko Takeda may publish in the future.
Co-authorship network of co-authors of Akiko Takeda
This figure shows the co-authorship network connecting the top 25 collaborators of Akiko Takeda.
A scholar is included among the top collaborators of Akiko Takeda 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 Akiko Takeda. Akiko Takeda is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Takeda, Akiko, et al.. (2018). Nonconvex Optimization for Regression with Fairness Constraints.. International Conference on Machine Learning. 2737–2746.22 indexed citations
12.
Ito, Naoki, Akiko Takeda, & Kim-Chuan Toh. (2017). A unified formulation and fast accelerated proximal gradient method for classification. Journal of Machine Learning Research. 18(1). 510–558.16 indexed citations
13.
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
14.
Katsumata, Shuichi & Akiko Takeda. (2015). Robust Cost Sensitive Support Vector Machine. International Conference on Artificial Intelligence and Statistics. 434–443.8 indexed citations
15.
Takeda, Akiko, et al.. (2015). Geometric intuition and algorithms for Ev-SVM. Journal of Machine Learning Research. 16(1). 323–369.3 indexed citations
16.
Iwata, Satoru, Yuji Nakatsukasa, & Akiko Takeda. (2014). Global Optimization Methods for Extended Fisher Discriminant Analysis. International Conference on Artificial Intelligence and Statistics. 411–419.3 indexed citations
17.
Kanamori, Takafumi, Akiko Takeda, & Taiji Suzuki. (2013). Conjugate relation between loss functions and uncertainty sets in classification problems. Journal of Machine Learning Research. 14(1). 1461–1504.9 indexed citations
18.
Takeda, Akiko. (2008). A modified algorithm for nonconvex support vector classification. 46–51.1 indexed citations
19.
Gotoh, Jun‐ya & Akiko Takeda. (2004). A linear classification model based on conditional geometric score. Terrestrial Environment Research Center (University of Tsukuba).17 indexed citations
20.
Takeda, Akiko. (1994). COMPARISON OF EXTRAVASCULAR LUNG WATER VOLUME WITH RADIOGRAPHIC FINDINGS IN DOGS WITH INCREASED PERMEABILITY PULMONARY EDEMA. Jūigaku kenkyū/Japanese journal of veterinary research. 42(1). 32–32.1 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.