| Wordrank: Learning word embeddings via robust ranking S Ji, H Yun, P Yanardag, S Matsushima, SVN Vishwanathan arXiv preprint arXiv:1506.02761, 2015 | 47 | 2015 |
| ITC-UT: Tweet Categorization by Query Categorization for On-line Reputation Management. M Yoshida, S Matsushima, S Ono, I Sato, H Nakagawa CLEF (Notebook Papers/LABs/Workshops) 170, 2010 | 36 | 2010 |
| Linear support vector machines via dual cached loops S Matsushima, SVN Vishwanathan, AJ Smola Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 21 | 2012 |
| Exact passive-aggressive algorithm for multiclass classification using support class S Matsushima, N Shimizu, K Yoshida, T Ninomiya, H Nakagawa Proceedings of the 2010 SIAM International Conference on Data Mining, 303-314, 2010 | 17 | 2010 |
| DS-MLR: exploiting double separability for scaling up distributed multinomial logistic regression P Raman, S Srinivasan, S Matsushima, X Zhang, H Yun, ... arXiv preprint arXiv:1604.04706, 2016 | 11 | 2016 |
| Traffic risk mining from heterogeneous road statistics K Moriya, S Matsushima, K Yamanishi IEEE Transactions on Intelligent Transportation Systems 19 (11), 3662-3675, 2018 | 7 | 2018 |
| Distributed stochastic optimization of the regularized risk S Matsushima, H Yun, X Zhang, SVN Vishwanathan arXiv preprint arXiv:1406.4363, 2014 | 5 | 2014 |
| Frequency-aware truncated methods for sparse online learning H Oiwa, S Matsushima, H Nakagawa Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 5 | 2011 |
| Scaling multinomial logistic regression via hybrid parallelism P Raman, S Srinivasan, S Matsushima, X Zhang, H Yun, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 4 | 2019 |
| Healing truncation bias: self-weighted truncation framework for dual averaging H Oiwa, S Matsushima, H Nakagawa 2012 IEEE 12th International Conference on Data Mining, 575-584, 2012 | 4 | 2012 |
| Web behavior analysis using sparse non-negative matrix factorization A Demachi, S Matsushima, K Yamanishi 2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016 | 3 | 2016 |
| Selective sampling-based scalable sparse subspace clustering S Matsushima, M Brbic Advances in Neural Information Processing Systems, 12416-12425, 2019 | 2 | 2019 |
| Sparse graphical modeling via stochastic complexity K Miyaguchi, S Matsushima, K Yamanishi Proceedings of the 2017 SIAM International Conference on Data Mining, 723-731, 2017 | 2 | 2017 |
| Traffic Risk Mining Using Partially Ordered Non-negative Matrix Factorization T Lee, S Matsushima, K Yamanishi 2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016 | 2 | 2016 |
| Feature-aware regularization for sparse online learning H Oiwa, S Matsushima, H Nakagawa Science China Information Sciences 57 (5), 1-21, 2014 | 2 | 2014 |
| Grafting for combinatorial binary model using frequent itemset mining T Lee, S Matsushima, K Yamanishi Data Mining and Knowledge Discovery 34 (1), 101-123, 2020 | 1 | 2020 |
| Statistical learnability of generalized additive models based on total variation regularization S Matsushima arXiv preprint arXiv:1802.03001, 2018 | 1 | 2018 |
| Discovering potential traffic risks in Japan using a supervised learning approach T Kobayashi, S Matsushima, T Lee, K Yamanishi 2017 IEEE International Conference on Big Data (Big Data), 948-955, 2017 | 1 | 2017 |
| Grafting for Combinatorial Boolean Model using Frequent Itemset Mining T Lee, S Matsushima, K Yamanishi arXiv preprint arXiv:1711.02478, 2017 | 1 | 2017 |
| Distributed Stochastic Optimization of Regularized Risk via Saddle-Point Problem S Matsushima, H Yun, X Zhang, SVN Vishwanathan Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 1 | 2017 |
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
