Joe Suzuki

1.1k total citations
52 papers, 449 citations indexed

About

Joe Suzuki is a scholar working on Artificial Intelligence, Statistics and Probability and Computational Theory and Mathematics. According to data from OpenAlex, Joe Suzuki has authored 52 papers receiving a total of 449 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 11 papers in Statistics and Probability and 8 papers in Computational Theory and Mathematics. Recurrent topics in Joe Suzuki's work include Bayesian Modeling and Causal Inference (23 papers), Machine Learning and Algorithms (10 papers) and Statistical Methods and Inference (8 papers). Joe Suzuki is often cited by papers focused on Bayesian Modeling and Causal Inference (23 papers), Machine Learning and Algorithms (10 papers) and Statistical Methods and Inference (8 papers). Joe Suzuki collaborates with scholars based in Japan, United States and Australia. Joe Suzuki's co-authors include Jun Kawahara, Shohei Shimizu, Boris Ryabko, Yoshinobu Kawahara, Flemming Topsøe, Akihiro Yamamoto, Takashi Washio, Yoshio Tanaka, Hiroki Sugano and Kuang‐Yao Lee and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Information Theory and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).

In The Last Decade

Joe Suzuki

42 papers receiving 405 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Joe Suzuki Japan 8 349 116 50 44 37 52 449
Scott Davies United States 8 362 1.0× 109 0.9× 26 0.5× 48 1.1× 38 1.0× 13 492
Thilo Mahnig France 8 383 1.1× 123 1.1× 18 0.4× 36 0.8× 31 0.8× 10 452
Jan Lemeire Belgium 9 172 0.5× 45 0.4× 42 0.8× 62 1.4× 24 0.6× 43 387
Shangzhu Jin China 12 228 0.7× 87 0.8× 42 0.8× 43 1.0× 43 1.2× 57 427
O. Uncu Canada 6 236 0.7× 40 0.3× 55 1.1× 19 0.4× 43 1.2× 12 330
Wen-Xiang Gu China 9 166 0.5× 96 0.8× 74 1.5× 39 0.9× 14 0.4× 48 350
Tian Gao United States 12 264 0.8× 50 0.4× 74 1.5× 13 0.3× 52 1.4× 37 351
Varun Kanade United States 9 200 0.6× 29 0.3× 92 1.8× 81 1.8× 23 0.6× 36 349
Daniel G. Schwartz United States 10 216 0.6× 81 0.7× 60 1.2× 58 1.3× 23 0.6× 44 323
Leonidas Pitsoulis Greece 10 100 0.3× 62 0.5× 26 0.5× 71 1.6× 12 0.3× 30 326

Countries citing papers authored by Joe Suzuki

Since Specialization
Citations

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

Fields of papers citing papers by Joe Suzuki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joe Suzuki

This figure shows the co-authorship network connecting the top 25 collaborators of Joe Suzuki. A scholar is included among the top collaborators of Joe Suzuki 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 Joe Suzuki. Joe Suzuki 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.
Sugano, Hiroki, Mitsunori Miki, Joe Suzuki, et al.. (2025). Quantum annealing-based route optimization for commercial AGV operating systems in large-scale logistics warehouses. Scientific Reports. 15(1). 44047–44047.
2.
Suzuki, Joe, et al.. (2024). Generalization of LiNGAM that Allows Confounding. 3540–3545.
4.
Lee, Kuang‐Yao, et al.. (2024). Functional linear non-Gaussian acyclic model for causal discovery. Behaviormetrika. 51(2). 567–588.
5.
Suzuki, Joe, et al.. (2023). Extending Hilbert–Schmidt Independence Criterion for Testing Conditional Independence. Entropy. 25(3). 425–425. 1 indexed citations
6.
Suzuki, Joe, et al.. (2022). Converting ADMM to a proximal gradient for efficient sparse estimation. Japanese Journal of Statistics and Data Science. 5(2). 725–745. 4 indexed citations
7.
Suzuki, Joe & Jun Kawahara. (2017). Branch and Bound for Regular Bayesian Network Structure Learing.. Uncertainty in Artificial Intelligence. 1 indexed citations
8.
Suzuki, Joe. (2016). A theoretical analysis of the BDeu scores in Bayesian network structure learning. Behaviormetrika. 44(1). 97–116. 23 indexed citations
9.
Washio, Takashi, et al.. (2011). Discovering causal structures in binary exclusive-or skew acyclic models. arXiv (Cornell University). 373–382. 3 indexed citations
10.
Suzuki, Joe. (2010). A Markov Chain Analysis of Genetic Algorithms: Large Deviation Principle Approach. Journal of Applied Probability. 47(4). 967–975.
11.
Suzuki, Joe. (2010). A Markov Chain Analysis of Genetic Algorithms: Large Deviation Principle Approach. Journal of Applied Probability. 47(4). 967–975. 3 indexed citations
12.
Suzuki, Joe. (2007). Miura conjecture on Affine curves. Osaka Journal of Mathematics. 44(1). 187–196. 3 indexed citations
13.
Suzuki, Joe, et al.. (2001). A Fast Jacobian Group Arithmetic Scheme for Algebraic Curve Cryptography. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 84(1). 130–139. 1 indexed citations
14.
Suzuki, Joe. (1999). Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique. IEICE Transactions on Information and Systems. 82(2). 356–367. 47 indexed citations
15.
Suzuki, Joe. (1999). Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 82(10). 2237–2245. 37 indexed citations
16.
Suzuki, Joe. (1998). A Relationship between Contex Tree Weighting and General Model Weighting Techniques for Tree Sources. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 81(11). 2412–2417.
17.
Suzuki, Joe. (1996). Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique.. International Conference on Machine Learning. 462–470. 28 indexed citations
18.
Suzuki, Joe. (1995). Some Notes on Universal Noiseless Coding. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 78(12). 1840–1847. 6 indexed citations
19.
Suzuki, Joe. (1993). Evaluations for Estimation of an Information Source Based on State Decomposition. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 76(7). 1240–1251.
20.
Suzuki, Joe. (1993). A Markov Chain Analysis on A Genetic Algorithm. international conference on Genetic algorithms. 146–154. 38 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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