Citations per year, relative to Issei Sato Issei Sato (= 1×)
peers
Zhihong Zhang
Countries citing papers authored by Issei Sato
Since
Specialization
Citations
This map shows the geographic impact of Issei Sato'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 Issei Sato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Issei Sato more than expected).
This network shows the impact of papers produced by Issei Sato. 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 Issei Sato. The network helps show where Issei Sato may publish in the future.
Co-authorship network of co-authors of Issei Sato
This figure shows the co-authorship network connecting the top 25 collaborators of Issei Sato.
A scholar is included among the top collaborators of Issei Sato 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 Issei Sato. Issei Sato is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sato, Issei, et al.. (2021). A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima. International Conference on Learning Representations.3 indexed citations
3.
Xu, Zijian, et al.. (2019). Solving NP-Hard Problems on Graphs by Reinforcement Learning without Domain Knowledge.. arXiv (Cornell University).10 indexed citations
Khan, Mohammad Emtiyaz, et al.. (2018). Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling.. International Conference on Artificial Intelligence and Statistics. 1108–1116.2 indexed citations
6.
Sato, Issei, et al.. (2018). Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks. arXiv (Cornell University). 31. 6541–6550.25 indexed citations
7.
Sato, Issei, et al.. (2018). Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model. International Conference on Machine Learning. 2147–2156.
8.
Tokui, Seiya & Issei Sato. (2017). Evaluating the Variance of Likelihood-Ratio Gradient Estimators. International Conference on Machine Learning. 3414–3423.2 indexed citations
9.
Sato, Issei, et al.. (2017). Variational Inference based on Robust Divergences. International Conference on Artificial Intelligence and Statistics. 813–822.3 indexed citations
10.
Sato, Issei, et al.. (2017). Bayesian Nonparametric Poisson Process Allocation. arXiv (Cornell University).
11.
Minami, Kentaro, et al.. (2016). Differential Privacy without Sensitivity. Neural Information Processing Systems. 29. 956–964.7 indexed citations
12.
Nakajima, Shinichi, et al.. (2014). Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP. Neural Information Processing Systems. 27. 1224–1232.3 indexed citations
13.
Sato, Issei & Hiroshi Nakagawa. (2014). Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process. International Conference on Machine Learning. 982–990.18 indexed citations
14.
Sato, Issei, Hisashi Kashima, & Hiroshi Nakagawa. (2014). Latent Confusion Analysis by Normalized Gamma Construction. International Conference on Machine Learning. 1116–1124.1 indexed citations
Sato, Issei, et al.. (2013). Multi-armed Bandit Problem with Lock-up Periods. Asian Conference on Machine Learning. 100–115.4 indexed citations
17.
Sato, Issei, et al.. (2012). Reducing Wrong Labels in Distant Supervision for Relation Extraction. Meeting of the Association for Computational Linguistics. 721–729.112 indexed citations
Sato, Issei & Hiroshi Nakagawa. (2006). Mining Semi-structure for Text with Dependency Structure. IEICE Technical Report; IEICE Tech. Rep.. 106(149). 161–166.2 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.