Taiji Suzuki
- Computational Mathematics top 1%
- Statistics and Probability top 1%
- Statistical Methods and Inference 24
- Artificial Intelligence top 1%
- Neural Networks and Applications 13
- Stochastic Gradient Optimization Techniques 12
- Bayesian Methods and Mixture Models 9
- Machine Learning and Algorithms 8
- Domain Adaptation and Few-Shot Learning 8
- Signal Processing top 5%
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- Face and Expression Recognition 12
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- Sparse and Compressive Sensing Techniques 32
Taiji Suzuki
89 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 122
- Computational Mathematics 115
- Statistics and Probability 370
- Artificial Intelligence 947
- Signal Processing 240
- Computer Vision and Pattern Recognition 422
Countries citing papers authored by Taiji Suzuki
This map shows the geographic impact of Taiji 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 Taiji Suzuki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taiji Suzuki more than expected).
Fields of papers citing papers by Taiji Suzuki
This network shows the impact of papers produced by Taiji 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 Taiji Suzuki. The network helps show where Taiji Suzuki may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Taiji Suzuki, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2022 | 5 | |
| 3 | Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime | 2021 | 1 |
| 4 | Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space | 2021 | 2 |
| 5 | Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks | 2020 | 1 |
| 6 | Graph Neural Networks Exponentially Lose Expressive Power for Node Classification | 2020 | 32 |
| 7 | Understanding Generalization in Deep Learning via Tensor Methods | 2020 | 1 |
| 8 | Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees | 2020 | 1 |
| 9 | On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective | 2019 | 4 |
| 10 | Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables | 2018 | 4 |
| 11 | Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization | 2017 | 4 |
| 12 | Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines | 2017 | 5 |
| 13 | Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning | 2016 | 2 |
| 14 | Conjugate relation between loss functions and uncertainty sets in classification problems | 2013 | 9 |
| 15 | PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model | 2012 | 10 |
| 16 | Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning | 2011 | 8 |
| 17 | Density Ratio Estimation : A Comprehensive Review (Statistical Experiment and Its Related Topics) | 2010 | 3 |
| 18 | A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices | 2010 | 15 |
| 19 | Regularization Strategies and Empirical Bayesian Learning for MKL | 2010 | 1 |
| 20 | Conditional Density Estimation via Least-Squares Density Ratio Estimation | 2010 | 19 |
About Taiji Suzuki
Taiji Suzuki is a scholar working on Computational Mathematics, Statistics and Probability and Artificial Intelligence, having authored 96 papers that have together received 1.8k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (32 papers), Statistical Methods and Inference (24 papers), Neural Networks and Applications (13 papers), Face and Expression Recognition (12 papers), Stochastic Gradient Optimization Techniques (12 papers), Bayesian Methods and Mixture Models (9 papers), Machine Learning and Algorithms (8 papers) and Domain Adaptation and Few-Shot Learning (8 papers). The work is most often cited by research in Computational Mathematics (115 citations), Statistics and Probability (370 citations) and Artificial Intelligence (947 citations). Taiji Suzuki has collaborated with scholars based in Japan, United States and Canada. Frequent co-authors include Masashi Sugiyama, Takafumi Kanamori, Ryota Tomioka, Paul von Bünau, Motoaki Kawanabe, Hisashi Kashima, Shinichi Nakajima, Jun Sese, Hirotaka Hachiya and Makoto Yamada. Their work appears in journals such as BMC Bioinformatics, The Annals of Statistics and Neural Computation.
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.